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9963ca278e Update .gitea/workflows/deploy-hugo.yaml
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2026-02-08 19:31:13 +01:00
eaa383a28a Trigger workflow test 2026-02-08 19:29:07 +01:00
58ab022661 add hugo deploy workflow 2026-02-08 19:28:33 +01:00
02e3bf816e update skills 2026-02-08 19:20:56 +01:00
3cb9ddc440 fix typo contacts 2026-02-08 19:15:00 +01:00
b97d64b2ac update contact page 2026-02-08 19:09:23 +01:00
1fe6a5a4bf update skills page 2026-02-08 19:00:36 +01:00
1c379a8424 remove generated files from repo, update .gitignore to ignore generated files 2026-02-08 18:57:54 +01:00
ac6e5ea8cf Fix hugo-coder submodule 2026-02-08 18:53:33 +01:00
b590081d94 update gamma tracking review project page to fix math rendering 2025-12-02 22:22:18 +01:00
f2b8941101 add gamma tracking lit review 2025-12-02 22:20:37 +01:00
55a3a15e66 update skills page 2025-12-02 06:36:57 +01:00
9245fe30d1 add entropic rubber band simulation project 2025-11-27 10:48:27 +01:00
9ae29e83da add hierarchical model project page 2025-11-26 16:01:36 +01:00
b4c0c2a115 update tagline 2025-10-11 16:12:01 +02:00
eaa9d37528 add favicon 2025-09-28 17:47:13 +02:00
7aa3e5e211 add favicon 2025-09-28 17:41:24 +02:00
3ecadf8860 update skills 2025-09-28 17:30:09 +02:00
00136b38bd remove old CV page 2025-09-28 17:24:28 +02:00
fc46e769a8 Point CV menu element to PDF instead of a page. 2025-06-15 17:17:24 +02:00
a0c4a63c7d update pfp 2025-06-15 17:01:21 +02:00
a22a8afb5d fix typo in cv 2025-06-09 10:28:43 +02:00
6fc51fad46 Fix typo in skills 2025-06-09 10:28:06 +02:00
0958003a69 update tagline 2025-06-09 10:24:27 +02:00
874410c870 add hpc2n workshop to skills 2025-06-09 10:24:27 +02:00
cf15d87283 fix inconsistencies on CV page 2025-06-09 10:24:27 +02:00
2a465a28df Update README.md 2025-06-09 10:14:25 +02:00
a0f29eef6b add biome class project 2025-06-09 10:13:46 +02:00
1e4216f921 Add new project 2025-05-14 22:16:51 +02:00
fed71f657d update skills 2025-05-14 14:55:50 +02:00
3df08b0c9a update bsc thesis page 2025-05-14 14:54:32 +02:00
4bd5cf82a9 update cv 2025-05-14 14:44:30 +02:00
b94f7ca725 Update projects 2025-05-14 14:44:10 +02:00
105 changed files with 340 additions and 30784 deletions

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name: Deploy Hugo Site
on:
push:
branches:
- test-actions
jobs:
deploy:
runs-on: self-hosted
steps:
- name: Checkout repository
uses: actions/checkout@v3
- name: Setup Hugo
uses: peaceiris/actions-hugo@v2
with:
hugo-version: '0.147' # specify your Hugo version
- name: Build Hugo site
run: hugo --destination /home/pim/website-live
- name: Sync to live
run: rsync -av --delete ./public/ /home/pim/website-live/

2
.gitignore vendored Normal file
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public/
resources/

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.gitmodules vendored Normal file
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[submodule "themes/hugo-coder"]
path = themes/hugo-coder
url = https://github.com/luizdepra/hugo-coder.git

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# PimNel.com Hugo website
Visit at https://pimnel.com.

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main:
- identifier: cv
name: CV
url: /en/cv/
url: https://git.pimnel.com/pim/CV/raw/branch/main/cv.pdf
weight: 1
- identifier: skills
name: Skills
@ -57,7 +57,7 @@ params:
colorScheme: "auto"
hidecolorschemetoggle: false
author: "Pim Nelissen"
info: ["Master's student in Computational Science/Physics", "B.Sc. Physics"]
info: ["Computational Physics student in Nuclear Science"]
avatarURL: "images/avatar.jpg"
since: 2024
social:

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slug = "contact"
+++
Email: pi0274ne-s@student.lu.se
Email: pi0274ne-s@student.lu.se
Phone: See my [CV](https://git.pimnel.com/pim/CV/raw/branch/main/cv.pdf).

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title = "CV"
slug = "cv"
+++
<head>
<link rel="stylesheet" href="/css/timeline.css">
</head>
## 🎓 Education
<div class="timeline">
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">2024 - <i>now</i></div>
<div class="timeline-content">
<h4>M.Sc. in Computational Science (Physics)</h4>
<p><i class="fas fa-university"></i> Lund University, Sweden 🇸🇪</p>
<p><code>Numerical ODE/PDE solvers</code> <code>MATLAB</code> <code>Monte Carlo</code> <code>Subatomic Physics</code></p>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">2020 - 2024</div>
<div class="timeline-content">
<h4>B.Sc. in Physics</h4>
<p><i class="fas fa-university"></i> Lund University, Sweden 🇸🇪</p>
<p><code>Python</code> <code>Deep learning</code> <code>Monte Carlo</code> <code>C#</code> <code>SQL</code></p>
<li class="timeline-content">Bachelor thesis: <i>"Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains"</i>, Lund University Publications, 2024. <a href="http://lup.lub.lu.se/student-papers/record/9168893" target="_blank">Open Access</a></li>
</div>
</div>
</div>
---
## 💼 Professional Experience
<div class="timeline">
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">2018 - 2020</div>
<div class="timeline-content">
<h4>Sales Specialist</h4>
<p><i class="fas fa-briefcase"></i> Jumbo Supermarkten, Netherlands 🇳🇱</p>
<p><i class="fas fa-lightbulb"></i> <code>Leadership</code></p>
<li class="timeline-content">Managed product presentation and customer experience.</li>
<li class="timeline-content">Occasionally led a team of 35 employees.</li>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">2015 - 2018</div>
<div class="timeline-content">
<h4>Sales Assistant</h4>
<p><i class="fas fa-university"></i> Jumbo Supermarkten, Netherlands 🇳🇱</p>
</div>
</div>
</div>
---
## 🙌 Volunteering
<div class="timeline">
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Jun - Aug 2023</div>
<div class="timeline-content">
<h4>System Administrator</h4>
<p><i class="fas fa-server"></i> DFRI, Sweden 🇸🇪</p>
<p><i class="fas fa-lightbulb"></i> <code>GNU/Linux</code> <code>Docker</code> <code>Nextcloud</code></p>
<li class="timeline-content">Maintained Linux infrastructure for a digital rights non-profit.</li>
<li class="timeline-content">Migrated Nextcloud to containerised Docker instance.</li>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">2013 - 2014</div>
<div class="timeline-content">
<h4>Digital Literacy Instructor</h4>
<p><i class="fas fa-book"></i> City Library Oss, Netherlands 🇳🇱</p>
<p><i class="fas fa-lightbulb"></i> <code>Teaching</code> <code>Organising</code> <code>Nextcloud</code></p>
<li class="timeline-content">I organised and taught a digital skills course to uplift senior citizens in the community.</li>
</div>
</div>
</div>
## 📖 Publication
**Bachelors Thesis**
**P. Nelissen**, *Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains*, Lund University, 2024.
---
## 👤 About Me
I enjoy both in-depth technical challenges and broader scientific learning. Im an avid reader, enjoy the outdoors, and thrive in environments that balance curiosity, creativity, and rigor.
---

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+++
title = "Projects"
slug = "projects"
+++
Lorem

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+++
authors = ["Pim Nelissen"]
title = "Hierarchical Bayesian regression model for CO2 emissions and energy usage"
date = "2025-10-30"
math = true
+++
{{< notice tip >}} The report is available [here](/files/hierarhical-model-co2-energy.pdf). {{< /notice >}}
# Reflection
For a course in reproducible data science and statistical learning, the final project was to find any statistical method from the course and apply it to any given dataset, In particular, Bayesian hierarchical modeling interested me.
# Summary
I decided to use it to figure out whether a lower share of fossil fuel production in the energy mix results in a decoupling of energy usage and CO2 emissions. In other words: does energy usage have reduced effect on emissions when fossil fuel share is reduced? The model is
$$
\begin{equation}
C_{ij} = \beta_{0} + \beta_{1} E_{ij} + \beta_{2} F_{ij} + \beta_{3} (E_{ij} \cdot F_{ij}) + b_{0j} + b_{3j} (E_{ij} \cdot F_{ij}) + \varepsilon_{ij}
\end{equation}
$$
where $C_{ij}$ is the CO2 emissions per capita for observation $i$ in region $j$, $E_{ij}$ is the energy usage per capita for observation $i$ in region $j$, $F_{ij}$ is the fossil fuel share in the energy mix for observation $i$ in region $j$, $\beta_0, \beta_1, \beta_2, \beta_3$ are the fixed effect parameters,
$b_{0j}, b_{3j}$ are random effects for region $j$ and
$\varepsilon_{ij}$ is an error term. The hypothesis test is formulated as follows:
$$
\begin{align*}
H_0 &: \beta_3 = 0 \\\\
H_1 &: \beta_3 > 0
\end{align*}
$$
where
- $H_0$: The interaction effect between energy usage and fossil fuel share on CO₂ emissions is zero (no effect).
- $H_1$: The interaction effect between energy usage and fossil fuel share on CO₂ emissions is positive.
The test is conducted with a significance level of $\alpha = 0.05$. The goal is to determine whether the 95% credible interval for $\beta_3$ excludes zero. If $0 \notin \text{CI}_{\beta_3}$, the null hypothesis $H_0$ is rejected in favor of $H_1$.
# Results
{{< figure
src="/images/bayesian_hierarhical_data.png"
caption="Figure 1: Example of the data."
>}}
The posterior of $\beta_3$ has shows that $E[\beta_3] = 0.29$ with a 95% credible interval of $[0.22, 0.35]$. The hypothesis test shows that the probability of $P(\beta_3 = 0 | \text{data}) = 0$ while $P(\beta_3 \geq 0 | \text{data}) = 1$. However, when looking at regional variability accounted for with regression parameter $b_{3j}$ (see Figure 2), the reality is that although we may see an overall positive global correlation, this does not necessarily extend to every country.
{{< figure
src="/images/bayesian_hierarhical_dot_whisker.png"
caption="Figure 2: Regional interaction effect $b_{3j}$ for each region, including uncertainties, in a dot-whisker plot. The red line shows $E[\beta_3] = 0.29$, the mean global effect."
>}}
The result for global effect parameter $\beta_3$ suggests that reducing fossil fuel share in the energy mix could decouple energy usage from CO2 emissions. However, the large variability and uncertainty at the regional level, with some regions/countries even showing opposite correlation, highlights the complexity of drawing definitive conclusions. Improvements could be: accounting for the temporal structure in the data. Also, some regions had limited data available which means large uncertainties, with many of those so large that the null-hypothesis could not be rejected on a regional level. Finally, it is important to highlight that the interaction term is agnostic in terms of causality and merely confirms a joint effect. It does not mean that lower fossil fuel share truly causes the decoupling of energy usage and CO2 emissions.

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+++
authors = ["Pim Nelissen"]
title = "Random forest models for the prediction of biome types and climate variables"
date = "2024-11-14"
+++
{{< notice tip >}} The report is available [here](/files/random-forests-biomes.pdf). {{< /notice >}}
# Reflection
This project was a big learning moment when it comes selecting training and testing datasets appropriately in statistical learning. The model can only ever be as good as the data we use. It was one of the first times working with geographical, grid-based data, which was also interesting. Since all of the data worked with was directly fed from a model, it's also important to know the limits of one's original model which provided the data. Sometimes, the problem may not be our classifier or regression model, but simply that we did not have enough, or the right, information to properly distinguish data in the first place.
# Summary
In this project, I developed some random forest models to predict biome classes (both binary and multi-class) as well as two continuous climate variables: vegetation carbon pool (VegC) and net primary productivity (NPP). To adress class imbalances, I used SMOTE up-sampling, which notably improved recall for underrepresented classes. By grid-search cross-validation I tried to tune the models better. Performance was evaluated using accuracy, precision, recall, and F₁ score for classifiers, and RMSE for regressors. Finally, I analyzed feature importance to better understand which climate variables, such as seasonal precipitation or extreme temperatures, were driving the model predictions.
# Results
The binary biome classifier achieved up to 85.7% accuracy after SMOTE resampling, effectively distinguishing between temperate deciduous and mixed forests, with winter temperature and autumn precipitation emerging as key predictors. The multi-class classifier reached a weighted F₁ score of around 0.65, although it struggled to separate closely related biomes, reflecting the continuity between the biomes proves challenging for ML to solve. The regression models performed well overall, but revealed spatial biases around coastal and desert areas, suggesting the need to account for additional local processes like soil variability or ocean influences.

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+++
authors = ["Pim Nelissen"]
title = "Machine learning applied to radioactive decay data (Bachelor thesis)"
date = "2024-06-27"
+++
{{< notice tip >}} The paper is available for [open access](http://lup.lub.lu.se/student-papers/record/9168893). {{< /notice >}}
{{< notice tip >}} The code I wrote for the decay chain simulations is openly available on my [Git instance](https://git.pimnel.com/pim/shn-decay-chains). {{< /notice >}}
# Reflection
This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.
On an intellectual level, it was a challenge in the beginning to adjust to the academic writing style of papers. Information is more dense and sometimes not explained in detail, referring the reader to some other paper. This trail of research papers that one can go down, even in a small topic as the one I was researching, is truly eye-opening to me. The amount of information that is out there is unimaginable to me. The project also introduced me to new applications of statistics and tested some old statistical knowledge, such as that on exponential distributions, statistical moments like variance and higher order ones. On top of that, I enjoyed learning about Monte Carlo simulations. I had always figured this term to sound complicated, but to learn that it is a rather easy methodology which has such strong use cases in various real-world applications was very interesting to learn. While not the main focus of my thesis, I also enjoyed researching the experimental background, of the process of superheavy synthesis and how detection is done.
# Official abstract
Experimental nuclear structure data coming from superheavy nuclei synthesis experiments often consists of correlated alpha decay chains. In the absence of neutron detectors - which would fully characterize the exit channel after the fusion-evaporation reaction - the sequence of decay energies and half-lives are the 'fingerprint' of the exit channel itself. Experimental data in this region is sparse, and its interpretation can be liable to error or confirmation bias. A so-called "Schmidt test" is a method for determining the congruence of correlation times for a set of measurements of one decay step. Its outcome is not always entirely conclusive, however. This study evaluates the congruence derived from the Schmidt test using Monte Carlo simulated data with various level of contamination from incongruent data. Furthermore, the study also includes the evaluation of congruence of data stemming from single decays and multi-step decay chains. A multi-layer perceptron was trained on extracted features from simulated decay chain sets with one step. The Schmidt test performs well with larger decay sets and when the half-life of the contaminating species is longer than the original species by a factor 5 or 10. However, the test performs poorly in low counting statistics, where few recorded decay times are available. The newly proposed machine learning model outperforms the Schmidt test in certain high statistics scenarios, but also fails when few decay times are available. Its performance is also poor when the half-life of the contaminant is shorter than the original half-life. The learning behaviour of the model is analysed, showing significant contributions from higher statistical moments in training. Future work involves including chain correlations across multiple steps, alpha decay energies, as well as the potential use of alternative machine learning models.
# Results
One of the key results of the thesis was the heatmap in Figure 1, showing how the amount of contamination in a decay time data set (that is, number of decay times from a species other than the one we are interested in) affects the Schmidt test and its conclusions. This was tested for various factors of contaminant half lives. The result is that the Schmidt test seems rather unsensitive to any contaminants with a half life on the same order of magnitude as the species we seek to study. This was further motivation to look beyond the Schmidt test, which solely relies on variance of the dataset, to see if other statistical methods (e.g. machine learning) could be used.
{{< figure
src="/images/st_factors.png"
caption="Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination."
>}}

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authors = ["Pim Nelissen"]
title = "1D entropic rubber band - Monte Carlo re-weighting techniques"
date = "2025-10-27"
math = true
+++
{{< notice tip >}} The git repository, including source code and more detailed documentation, is available [here](https://gitlab.com/pimnelissen/entropic-spring). {{< /notice >}}
# Reflection
This project explores the statistical simulation of a simple 1D rubber band as a chain of links. It was intended for exploring Monte Carlo simulations and particularly re-weighting techniques.
# Theory
In statistical terms we can think of the rubber band as a series of fixed-length segments, which have a positive or negative direction. Then, a rubber band of $N$ segments or 'links' has $n^+$ positive and $n^-$ negative links. The total length $L$ is then
$$L = a(2n^+ - N )$$
With no force applied corresponds to a random assignment which means that $E[L] = 0$.
There are many microstates which correspond to one macrostate of $L$, namely
$$
\Omega(N, n^+) = \binom{N}{n^+} = \frac{N!}{n^+!(N-n^+)!}
$$
with this, we have that the true distribution of lengths will be
$$P(L) = \Omega(N, n)/2^N$$
## Results
Here follows a brief display of the results. Interpretation is in each figure caption.
### Rubber band with no force
As a sanity test, the first task consisted of simply generating a rubber band with no force applied.
{{< figure
src="/images/task1_histogram.png"
caption="**Figure 1:** *Histogram of sampled lengths for $M=10^6$ samples of a rubber band consisting of $N=100$ length $a=1$ links. $P(L) = \Omega(N, n)/2^N$ represents the true distribution of lengths for a given $N$ and $n=(N+L/a)/2$. $\hat{P}(L)$ represents the normalised histogram for lengths $L$ of the sampled rubber bands. The $\chi^2$ test was done for all bins with more than 5 counts, and shows good agreement between theory and sample.*" >}}
{{< figure
src="/images/task1_ratio_plot.png"
caption="**Figure 2:** *$P(L)/\hat{P}(L)$ for various lengths $L/a$. As expected, the ratio is close to $1$ around $L=0$ where we have sufficient statistics. This latter statement is not true at the tails, where we see the sample probability start to diverge from the true probability.*" >}}
### Rubber band with force (weighting)
Suppose we want to apply a force $f$. Obviously one way to get a updated $\hat{P}(L)$ is to resample with some force applied. But this is inefficient, if we already have some previous sample. One way to avoid having to resample is weighting the original unbiased distribution by applying a Boltzmann weight to each microstate
$$\omega = \exp{(\beta fL)}, \quad \beta = 1/k_BT.$$
The new true distribution then becomes
$$
P_f(L) = \frac{\Omega(N, n) e^{\beta f L}}{Z(f)}, \quad
Z(f) = \sum_L \Omega(N, n) e^{\beta f L}
$$
{{< figure
src="/images/task2_histograms.png"
caption="**Figure 3:** *Histograms for the weighted distributions with $k_BT=a=1$. In green is the original, unbiased distribution $\hat{P}(L)$. Red and blue show the sampled and true distributions with applied weights, according to the description above this figure. What we see is that weighting only works with good statistics. When the force $f$ causes $\hat{P}(L)$ to shift too much beyond the original distribution, the lack of statistics (available samples to weight) causes incorrect results. The figure also shows $\mu_{eff}$, the effective percentage of samples used in the weighted distribution, for each force applied.*" >}}
{{< figure
src="/images/task2_ratio_plots.png"
caption="**Figure 4:** *Ratio plots for the weighted distributions. We see that the sampled distribution after weighting becomes unrepresentative of the true underlying distribution for higher forces $f$.*" >}}
{{< figure
src="/images/task2_force_vs_mueff.png"
caption="**Figure 5:** *A plot of force against $\mu_{eff}$. Clearly one can see that effective sample size decreases exponentially with increasing $f$, so the weighting technique, while very efficient, only work with sufficient overlap between the source and target distributions, in this case, small $f$.*" >}}
### Resampling and approximating expected length as function of force
When weighting breaks down we must resample. How can one implement this? One way is to bias the sampling. Instead of $p_+ = p_- = 0.5$, we let $p_+$ depend on the force $f$. This is given by
$$p_+(f) = \frac{w_+}{w_+ + w_-} = \frac{e^{\beta f a}}{e^{\beta f a} + e^{-\beta f a}} = \frac{1}{2}(1 + \tanh(\beta f a))$$
and then of course $p_-(f) = 1-p_+(f)$.
One can see here that when $f=0$ the above reduces to the simple $p_+ = p_- = 0.5$. Now, we then have that the expected length for some applied force is
$$ \langle L \rangle (f) = N a \tanh(\beta fa).$$
If $\beta fa \ll 1$, the small force approximation
$$\langle L \rangle (f ) \approx \frac{Nfa^2}{k_BT}$$
may be used. In this final simulation, we seek to find out how the mean lengths $L$ from biased sampling according to the force-dependent $p_+(f)$ align with the theoretical expected value and the small force approximation.
{{< figure
src="/images/task3_true_vs_sim.png"
caption="**Figure 6:** *$\langle L \rangle (f)$ for various forces $f$. We have $M=10^3$ samples for each $f$, with $k_BT=a=1$ and $N=100$, which gives a $k_\{eff\}$ of $100$. The analytical formulas are in red and are described above this figure. We can see excellent agreement with the analytical $\langle L \rangle(f)$ across a large range of $f$. However, the small force approximation in the lower figure starts to diverge at $f > 0.3$. A linear fit to $\langle \hat\{L\} \rangle(f<0.3)$ gives us an estimated $k_\{eff\} \approx 96.93$*." >}}
{{< figure
src="/images/task3_true_vs_sim_error.png"
caption="**Figure 7:** To clarify Figure 6, the absolute difference between the sampled $\langle \hat{L} \rangle(f)$ and $\langle L \rangle(f)$ as well as $\langle L \rangle(f)$ with the small force approximation. We can very clearly see that the small force approximation breaks down around an $f$ of $0.3-0.4$." >}}

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+++
authors = ["Pim Nelissen"]
title = "Coupled Pendula & The Kuramoto Model (1st year experimental project)"
date = "2021-06-15"
+++
{{< notice tip >}} Lund University wrote an article about our project. You can find it [here](https://www.fysik.lu.se/artikel/pandemisakert-nar-fysikstudenter-redovisar).
(Note: the article is written in Swedish). {{< /notice >}}
{{< youtube roK2gRDHWeE >}}

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+++
authors = ["Pim Nelissen"]
title = "Developments and Implementation of gamma-ray Tracking in AGATA: A literature review"
date = "2025-05-27"
math = true
+++
{{< notice tip >}} The report is available [here](/files/gamma-ray-tracking.pdf). {{< /notice >}}
# Reflection
As a final project in my Modern Subatomic Physics course, we were to do a small literature review of anything from the course. I decided to write about $\gamma$-ray tracking algoritm development mainly around the European Advanced GAmma Tracking Array AGATA.
# Summary
Out of EUROBALL and GAMMASPHERE, the state of the art High-Purity Germanium detectors of the 20th century, came the development of The Advanced GAmma Tracking Array. AGATA is a European detector array which uses highly segmented HPGe crystals and the concept of $\gamma$-ray tracking to reconstruct the chain of Compton scattering events that $\gamma$-rays in nuclear structure studies can go through before fully depositing their remaining energy in a HPGe crystal through the photoelectric effect. The paper briefly discusses the conceptual background of gamma spectroscopy and its use cases, the different physical interactions of $\gamma$-rays with matter, and the experimental challenges with this type of spectroscopy. The developments that led up to the construction of AGATA are discussed. Followed by this historic overview, physical background and motivation for $\gamma$-ray tracking, the computational procedure and algorithms involved with $\gamma$-ray tracking are discussed in detail. Performance of AGATA with these algorithms is discussed and compared with previous detector setups. On top of that, performance between the different procedures is assessed too. Recent developments in tracking, such as self-calibrating algorithms and graph neural networks, are briefly discussed and compared to existing methods.
# Conclusion
The past 20 years have seen significant development in the space of $\gamma$-ray tracking. From EUROBALL to AGATA with forward tracking, we observed a significant improvement in efficiency and a notable improvement in P/T ratio, but there is room for further improvement. Recent studies using probability based calibration algorithms and neural networks show promising results, but more research needs to be done in order to make progress and further improve the spectroscopic results. Nonetheless, AGATA remains the state of the art γ-tracking detector array with relevancy to todays nuclear structure studies as well as into the future.

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slug = "skills"
+++
## Core Competencies
## 🌟 Core Competencies
From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My [bachelor degree thesis](http://lup.lub.lu.se/student-papers/record/9168893) is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.
From my background in physics and computational science, I have a strong foundation in using numerical methods and statistical methods for solving physical problems. Examples of such methods include numerical methods for differential equations, Monte Carlo simulations, statistical learning including Bayesian methods and machine learning, both supervised and unsupervised approaches.
`Python` `MATLAB` `TensorFlow` `C#` `SQL` `Git` `GNU/Linux`
I am particularly interested in using those skills for peaceful applications of nuclear science. My [bachelor degree thesis](http://lup.lub.lu.se/student-papers/record/9168893) was about applying machine learning on radioactive decay data. I am proficient in Python, experienced with R and MATLAB, and have some exposure to C++, C# and SQL. I am very comfortable with GNU/Linux and Git workflows.
My master degree thesis will be on furthering the development of a Bayesian algorithm for multisource detection in mobile gamma spectroscopy.
{{< notice tip >}}A selection of code can be found on my [git instance](http://git.pimnel.com).{{< /notice >}}
---
## 🧪 Attended Workshops
I like to attend (online) workshops in order to broaden my knowledge or refresh some old skills. Below is a timeline of selected workshops which I have followed in the past.
## 🧪 Workshops & Summer Schools
I like to attend summer schools and (online) workshops in order to broaden my knowledge or refresh some old skills. Below is a timeline of selected workshops which I have followed in the past.
<div class="timeline">
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Jan 2026</div>
<div class="timeline-content">
<h4>Modern Python in Depth</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.sc.lrz.de/" target="_blank">Leibniz Supercomputing Centre</a>, Germany 🇩🇪</p>
<p>Online workshop.</p>
<p><code>Scientific computing</code> <code>Modern Python standard</code></p>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Dec 2025</div>
<div class="timeline-content">
<h4>Core Modeling For Core Design</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="http://www.great-pioneer.eu" target="_blank">GRE@T-PIONEeR</a> @ Chalmers University of Technology, Sweden 🇸🇪</p>
<p>One week course on-site at Chalmers. Learned the principles of probabilistic and deterministic methods for steady-state core modelling, applied to reference calculations, operations and safety analysis.</p>
<p><code>Reactor physics</code> <code>Nuclear engineering</code></p>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Sep 2025</div>
<div class="timeline-content">
<h4>ENEN2+ Nuclear Summer School</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="http://www.reak.bme.hu/en/home.html" target="_blank">Budapest University of Technology and Economics</a>, Hungary 🇭🇺</p>
<p>Participated in a week summer school at the BME Training reactor in Budapest, on scholarship from the European Nuclear Education Network. Experiments were focused on the principles of reactor physics; determining neutron flux, criticality experiments, and operations.</p>
<p><code>Reactor physics</code> <code>Experimental physics</code></p>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Jun 2025</div>
<div class="timeline-content">
<h4>High-performance Computing with Python</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.fz-juelich.de/en/ias/jsc" target="_blank">Jülich Supercomputing Centre</a>, Germany 🇩🇪</p>
<p>Online workshop.</p>
<p><code>HPC</code>
<code>Parallelization</code> <code>Scripting</code></p>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Jun 2025</div>
<div class="timeline-content">
<h4>Intermediate Bash and Linux</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.hpc2n.umu.se/" target="_blank">HPC2N, Umeå University</a>, Sweden 🇸🇪</p>
<p>Online workshop.</p>
<p><code>grep</code> <code>Sed and AWK</code> <code>Bash scripting</code></p>
</div>
</div>
<div class="timeline-item">
<div class="dot"></div>
<div class="timeline-date">Mar 2025</div>
<div class="timeline-content">
<h4>Bayesian Statistical Learning</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.fz-juelich.de/en/ias/jsc" target="_blank">Jülich Supercomputing Centre</a>, Germany 🇩🇪</p>
<p>Online workshop.</p>
<p><code>MCMC</code> <code>PyMC3</code> <code>Supercomputer usage</code></p>
</div>
</div>
@ -30,6 +86,7 @@ I like to attend (online) workshops in order to broaden my knowledge or refresh
<div class="timeline-content">
<h4>Programming with Fortran</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.sc.lrz.de/" target="_blank">Leibniz Supercomputing Centre</a>, Germany 🇩🇪</p>
<p>Online workshop.</p>
<p><code>Arrays</code> <code>Subroutines</code> <code>Derived Types</code> <code>I/O</code> <code>Performance considerations</code></p>
</div>
</div>
@ -39,10 +96,8 @@ I like to attend (online) workshops in order to broaden my knowledge or refresh
<div class="timeline-content">
<h4>Code & Collaborate: The FAIRytale of Software Development</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.cecam.org/" target="_blank">EPFL CECAM</a> & <a href="https://biont-training.eu/" target="_blank">BioNT</a>, Switzerland 🇨🇭</p>
<p>Online workshop.</p>
<p><code>Git (CLI)</code> <code>Continuous Integration</code> <code>Code Documentation</code></p>
</div>
</div>
</div>
## 🎓 Selection of courses
</div>

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<!DOCTYPE html>
<html lang="en">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>
CV · Pim Nelissen
</title>
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<meta name="author" content="Pim Nelissen">
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🎓 Education
Link to heading
M.Sc. in Computational Science (Physics)
Lund University
📅 Sep 2024 Present
Focus on numerical methods for ODEs/PDEs using MATLAB.
B.Sc. in Physics
Lund University
📅 Sep 2020 Jun 2024
Python Deep learning Monte Carlo C# SQL
Thesis: Machine Learning for Statistical Assessment of Radioactive Decay Chains
Topics: Monte Carlo simulation, deep learning, C#, SQL, Python
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Lund University
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Focus on numerical methods for ODEs/PDEs using MATLAB. B.Sc. in Physics
Lund University
📅 Sep 2020 Jun 2024
Python Deep learning Monte Carlo C# SQL
Thesis: Machine Learning for Statistical Assessment of Radioactive Decay Chains Topics: Monte Carlo simulation, deep learning, C#, SQL, Python 💼 Professional Experience Link to heading Sales Specialist
Jumbo Supermarkten Oss, Netherlands
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Lund University
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Python Deep learning Monte Carlo C# SQL
Thesis: Machine Learning for Statistical Assessment of Radioactive Decay Chains Topics: Monte Carlo simulation, deep learning, C#, SQL, Python 💼 Professional Experience Link to heading Sales Specialist
Jumbo Supermarkten Oss, Netherlands
📅 2018 2020">
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<p><strong>M.Sc. in Computational Science (Physics)</strong><br>
<em>Lund University</em><br>
📅 <em>Sep 2024 Present</em></p>
<ul>
<li>Focus on numerical methods for ODEs/PDEs using MATLAB.</li>
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<p><strong>B.Sc. in Physics</strong><br>
<em>Lund University</em><br>
📅 <em>Sep 2020 Jun 2024</em></p>
<p><code>Python</code> <code>Deep learning</code> <code>Monte Carlo</code> <code>C#</code> <code>SQL</code></p>
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<li>Thesis: <em>Machine Learning for Statistical Assessment of Radioactive Decay Chains</em></li>
<li>Topics: Monte Carlo simulation, deep learning, C#, SQL, Python</li>
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<em>Jumbo Supermarkten Oss, Netherlands</em><br>
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<p><strong>Bachelors Thesis</strong><br>
<strong>P. Nelissen</strong>, <em>Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains</em>, Lund University, 2024.<br>
📄 <a href="http://lup.lub.lu.se/student-papers/record/9168893" class="external-link" target="_blank" rel="noopener">Read Online</a></p>
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<p>I enjoy both in-depth technical challenges and broader scientific learning. Im an avid reader, enjoy the outdoors, and thrive in environments that balance curiosity, creativity, and rigor.</p>
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🎓 Education
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2024 - now
M.Sc. in Computational Science (Physics)
Lund University, Sweden 🇸🇪
Numerical ODE/PDE solvers MATLAB Monte Carlo Subatomic Physics
2020 - 2024
B.Sc. in Physics
Lund University, Sweden 🇸🇪
Python Deep learning Monte Carlo C# SQL
Bachelor thesis: &#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;, Lund University Publications, 2024. Open Access
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Sales Specialist
Jumbo Supermarkten, Netherlands 🇳🇱">
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<meta name="twitter:description" content="🎓 Education Link to heading 2024 - now M.Sc. in Computational Science (Physics) Lund University, Sweden 🇸🇪
Numerical ODE/PDE solvers MATLAB Monte Carlo Subatomic Physics
2020 - 2024 B.Sc. in Physics Lund University, Sweden 🇸🇪
Python Deep learning Monte Carlo C# SQL
Bachelor thesis: &#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;, Lund University Publications, 2024. Open Access 💼 Professional Experience Link to heading 2018 - 2020 Sales Specialist Jumbo Supermarkten, Netherlands 🇳🇱">
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Python Deep learning Monte Carlo C# SQL
Bachelor thesis: &#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;, Lund University Publications, 2024. Open Access 💼 Professional Experience Link to heading 2018 - 2020 Sales Specialist Jumbo Supermarkten, Netherlands 🇳🇱">
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<p><code>Numerical ODE/PDE solvers</code> <code>MATLAB</code> <code>Monte Carlo</code> <code>Subatomic Physics</code></p>
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<li class="timeline-content">Bachelor thesis: <i>"Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains"</i>, Lund University Publications, 2024. <a href="http://lup.lub.lu.se/student-papers/record/9168893" target="_blank">Open Access</a></li>
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Skills · Pim Nelissen
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From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My bachelor degree thesis is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.">
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<meta property="og:description" content="Core Competencies Link to heading From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My bachelor degree thesis is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.">
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Core Competencies
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<p>From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My <a href="http://lup.lub.lu.se/student-papers/record/9168893" class="external-link" target="_blank" rel="noopener">bachelor degree thesis</a> is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.</p>
<p><code>Python</code> <code>MATLAB</code> <code>TensorFlow</code> <code>C#</code> <code>SQL</code> <code>Git</code> <code>GNU/Linux</code></p>
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<p>I like to attend (online) workshops in order to broaden my knowledge or refresh some old skills. Below is a timeline of selected workshops which I have followed in the past.</p>
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<h4>Bayesian Statistical Learning</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.fz-juelich.de/en/ias/jsc" target="_blank">Jülich Supercomputing Centre</a>, Germany 🇩🇪</p>
<p><code>MCMC</code> <code>PyMC3</code> <code>Supercomputer usage</code></p>
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<h4>Programming with Fortran</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.sc.lrz.de/" target="_blank">Leibniz Supercomputing Centre</a>, Germany 🇩🇪</p>
<p><code>Arrays</code> <code>Subroutines</code> <code>Derived Types</code> <code>I/O</code> <code>Performance considerations</code></p>
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<h4>Code & Collaborate: The FAIRytale of Software Development</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.cecam.org/" target="_blank">EPFL CECAM</a> & <a href="https://biont-training.eu/" target="_blank">BioNT</a>, Switzerland 🇨🇭</p>
<p><code>Git (CLI)</code> <code>Continuous Integration</code> <code>Code Documentation</code></p>
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<description>&lt;h2 id=&#34;-education&#34;&gt;&#xA; 🎓 Education&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#-education&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;&lt;strong&gt;M.Sc. in Computational Science (Physics)&lt;/strong&gt;&lt;br&gt;&#xA;&lt;em&gt;Lund University&lt;/em&gt;&lt;br&gt;&#xA;📅 &lt;em&gt;Sep 2024 Present&lt;/em&gt;&lt;/p&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;Focus on numerical methods for ODEs/PDEs using MATLAB.&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;p&gt;&lt;strong&gt;B.Sc. in Physics&lt;/strong&gt;&lt;br&gt;&#xA;&lt;em&gt;Lund University&lt;/em&gt;&lt;br&gt;&#xA;📅 &lt;em&gt;Sep 2020 Jun 2024&lt;/em&gt;&lt;/p&gt;&#xA;&lt;p&gt;&lt;code&gt;Python&lt;/code&gt; &lt;code&gt;Deep learning&lt;/code&gt; &lt;code&gt;Monte Carlo&lt;/code&gt; &lt;code&gt;C#&lt;/code&gt; &lt;code&gt;SQL&lt;/code&gt;&lt;/p&gt;&#xA;&lt;ul&gt;&#xA;&lt;li&gt;Thesis: &lt;em&gt;Machine Learning for Statistical Assessment of Radioactive Decay Chains&lt;/em&gt;&lt;/li&gt;&#xA;&lt;li&gt;Topics: Monte Carlo simulation, deep learning, C#, SQL, Python&lt;/li&gt;&#xA;&lt;/ul&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;-professional-experience&#34;&gt;&#xA; 💼 Professional Experience&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#-professional-experience&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;&lt;strong&gt;Sales Specialist&lt;/strong&gt;&lt;br&gt;&#xA;&lt;em&gt;Jumbo Supermarkten Oss, Netherlands&lt;/em&gt;&lt;br&gt;&#xA;📅 &lt;em&gt;2018 2020&lt;/em&gt;&lt;/p&gt;</description>
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<!DOCTYPE html>
<html lang="nl">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>Pim Nelissen</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
<meta name="description" content="">
<meta name="keywords" content="">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="404 Page not found">
<meta property="og:url" content="//localhost:1313/nl/404.html">
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<meta property="og:title" content="404 Page not found">
<meta property="og:locale" content="nl">
<meta property="og:type" content="website">
<link rel="canonical" href="//localhost:1313/nl/404.html">
<link rel="preload" href="/fonts/fa-brands-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
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<link rel="stylesheet" href="/css/coder.css" media="screen">
<link rel="stylesheet" href="/css/coder-dark.css" media="screen">
<link rel="stylesheet" href="/css/timeline.css">
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<link rel="manifest" href="/site.webmanifest">
<link rel="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
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<body class="preload-transitions colorscheme-auto">
<div class="float-container">
<a id="dark-mode-toggle" class="colorscheme-toggle">
<i class="fa-solid fa-adjust fa-fw" aria-hidden="true"></i>
</a>
</div>
<main class="wrapper">
<nav class="navigation">
<section class="container">
<a class="navigation-title" href="//localhost:1313/nl/">
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</a>
<input type="checkbox" id="menu-toggle" />
<label class="menu-button float-right" for="menu-toggle">
<i class="fa-solid fa-bars fa-fw" aria-hidden="true"></i>
</label>
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<li class="navigation-item">
<a class="navigation-link " href="/nl/cv/">CV</a>
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<div class="error">
<h1>404</h1>
<h2>Pagina niet gevonden</h2>
<p>Sorry, deze pagina bestaat niet.<br />U kunt terugkeren naar de <a href="//localhost:1313/">thuispagina</a>.</p>
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<footer class="footer">
<section class="container">
©
2023 -
2025
Pim Nelissen
·
Aangedreven door <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
</section>
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<!DOCTYPE html>
<html lang="nl">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>Categorieën · Pim Nelissen</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
<meta name="description" content="">
<meta name="keywords" content="">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Categories">
<meta property="og:url" content="//localhost:1313/nl/categories/">
<meta property="og:site_name" content="Pim Nelissen">
<meta property="og:title" content="Categories">
<meta property="og:locale" content="nl">
<meta property="og:type" content="website">
<link rel="canonical" href="//localhost:1313/nl/categories/">
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<link rel="stylesheet" href="/css/coder.css" media="screen">
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<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" sizes="any">
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<link rel="alternate" type="application/rss+xml" href="/nl/categories/index.xml" title="Pim Nelissen" />
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<a class="navigation-link " href="/nl/cv/">CV</a>
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<section class="container taxonomy">
<header>
<h1 class="title">
<a class="title-link" href="//localhost:1313/nl/categories/">Categorieën</a>
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<section class="container">
©
2023 -
2025
Pim Nelissen
·
Aangedreven door <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
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<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
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<title>Categories on Pim Nelissen</title>
<link>//localhost:1313/nl/categories/</link>
<description>Recent content in Categories on Pim Nelissen</description>
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<language>nl</language>
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</rss>

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<!DOCTYPE html>
<html lang="nl">
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<title>Pim Nelissen</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
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<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Pim Nelissen">
<meta property="og:url" content="//localhost:1313/nl/">
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<meta property="og:title" content="Pim Nelissen">
<meta property="og:locale" content="nl">
<meta property="og:type" content="website">
<link rel="canonical" href="//localhost:1313/nl/">
<link rel="preload" href="/fonts/fa-brands-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-solid-900.woff2" as="font" type="font/woff2" crossorigin>
<link rel="stylesheet" href="/css/coder.css" media="screen">
<link rel="stylesheet" href="/css/coder-dark.css" media="screen">
<link rel="stylesheet" href="/css/timeline.css">
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" sizes="any">
<link rel="icon" type="image/png" href="/images/favicon-32x32.png" sizes="32x32">
<link rel="icon" type="image/png" href="/images/favicon-16x16.png" sizes="16x16">
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<link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png">
<link rel="manifest" href="/site.webmanifest">
<link rel="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
<link rel="alternate" type="application/rss+xml" href="/nl/index.xml" title="Pim Nelissen" />
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</head>
<body class="preload-transitions colorscheme-auto">
<div class="float-container">
<a id="dark-mode-toggle" class="colorscheme-toggle">
<i class="fa-solid fa-adjust fa-fw" aria-hidden="true"></i>
</a>
</div>
<main class="wrapper">
<nav class="navigation">
<section class="container">
<a class="navigation-title" href="//localhost:1313/nl/">
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</a>
<input type="checkbox" id="menu-toggle" />
<label class="menu-button float-right" for="menu-toggle">
<i class="fa-solid fa-bars fa-fw" aria-hidden="true"></i>
</label>
<ul class="navigation-list">
<li class="navigation-item">
<a class="navigation-link " href="/nl/cv/">CV</a>
</li>
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<a class="navigation-link " href="/nl/projects/">Projecten</a>
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<a class="navigation-link " href="/nl/contact/">Contact</a>
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</li>
<li class="navigation-item">
<a href="/en/">🇬🇧 English</a>
</li>
</ul>
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<div class="content">
<section class="container centered">
<div class="about">
<div class="avatar"><img src="/images/avatar.jpg" alt="avatar"></div>
<h1>Pim Nelissen</h1>
<h2>Physicist<br>Computational Scientist<br></h2>
<ul>
<li>
<a href="https://www.linkedin.com/in/pim-nelissen/" aria-label="LinkedIn" >
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</a>
</li>
<li>
<a href="https://git.pimnel.com/pim" aria-label="Gitea" >
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</a>
</li>
<li>
<a href="mailto:pi0274ne-s@student.lu.se" aria-label="Email" >
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</a>
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<a href="tel:&#43;46760226691" aria-label="Phone" >
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</div>
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</div>
<footer class="footer">
<section class="container">
©
2023 -
2025
Pim Nelissen
·
Aangedreven door <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
</section>
</footer>
</main>
<script src="/js/coder.js"></script>
</body>
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@ -1,11 +0,0 @@
<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
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<link>//localhost:1313/nl/</link>
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<generator>Hugo</generator>
<language>nl</language>
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</channel>
</rss>

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<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
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<xhtml:link
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hreflang="en"
href="//localhost:1313/en/categories/"
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href="//localhost:1313/nl/categories/"
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<loc>//localhost:1313/nl/</loc>
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href="//localhost:1313/en/"
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<xhtml:link
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hreflang="nl"
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<loc>//localhost:1313/nl/tags/</loc>
<xhtml:link
rel="alternate"
hreflang="en"
href="//localhost:1313/en/tags/"
/>
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hreflang="nl"
href="//localhost:1313/nl/tags/"
/>
</url>
</urlset>

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<!DOCTYPE html>
<html lang="nl">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>Labels · Pim Nelissen</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
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<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Tags">
<meta property="og:url" content="//localhost:1313/nl/tags/">
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<meta property="og:title" content="Tags">
<meta property="og:locale" content="nl">
<meta property="og:type" content="website">
<link rel="canonical" href="//localhost:1313/nl/tags/">
<link rel="preload" href="/fonts/fa-brands-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-solid-900.woff2" as="font" type="font/woff2" crossorigin>
<link rel="stylesheet" href="/css/coder.css" media="screen">
<link rel="stylesheet" href="/css/coder-dark.css" media="screen">
<link rel="stylesheet" href="/css/timeline.css">
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" sizes="any">
<link rel="icon" type="image/png" href="/images/favicon-32x32.png" sizes="32x32">
<link rel="icon" type="image/png" href="/images/favicon-16x16.png" sizes="16x16">
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<link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png">
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<link rel="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
<link rel="alternate" type="application/rss+xml" href="/nl/tags/index.xml" title="Pim Nelissen" />
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<body class="preload-transitions colorscheme-auto">
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<main class="wrapper">
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<a class="navigation-title" href="//localhost:1313/nl/">
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</a>
<input type="checkbox" id="menu-toggle" />
<label class="menu-button float-right" for="menu-toggle">
<i class="fa-solid fa-bars fa-fw" aria-hidden="true"></i>
</label>
<ul class="navigation-list">
<li class="navigation-item">
<a class="navigation-link " href="/nl/cv/">CV</a>
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Pim Nelissen
·
Byggd med <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
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View File

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