Update .gitea/workflows/deploy-hugo.yaml

.gitea/workflows/deploy-hugo.yaml

@@ -0,0 +1,24 @@
+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/

.gitignore

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+public/
+resources/

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "themes/hugo-coder"]
+	path = themes/hugo-coder
+	url = https://github.com/luizdepra/hugo-coder.git

README.md

@@ -1,2 +1,3 @@
-# website
+# PimNel.com Hugo website
 
+Visit at https://pimnel.com.

config.yml

@@ -1,28 +1,63 @@
 baseURL: ""
 languageCode: "en"
 defaultContentLanguage: "en"
+defaultContentLanguageInSubdir: true
 enableEmoji: true
 title: Pim Nelissen
 theme: hugo-coder
 
-menu:
-  main:
-    - identifier: cv
-      name: CV
-      url: /cv/
-      weight: 10
-    - identifier: projects
-      name: Projects
-      url: /projects/
-      weight: 20
-    - identifier: contact 
-      name: Contact
-      url: /contact/
-      weight: 30
+markup:
+  goldmark:
+    renderer:
+      unsafe: true
+
+languages:
+  en:
+    languageName: ":uk: English"
+    weight: 1
+    contentDir: "content/en"
+    menu:
+      main:
+        - identifier: cv
+          name: CV
+          url: https://git.pimnel.com/pim/CV/raw/branch/main/cv.pdf
+          weight: 1
+        - identifier: skills
+          name: Skills
+          url: /en/skills/
+          weight: 2
+        - identifier: projects
+          name: Projects
+          url: /en/projects/
+          weight: 3
+        - identifier: contact 
+          name: Contact
+          url: /en/contact/
+          weight: 4
+  # nl:
+  #   languageName: ":netherlands: Nederlands"
+  #   weight: 2
+  #   contentDir: "content/nl"
+  #   menu:
+  #     main:
+  #       - identifier: cv
+  #         name: CV
+  #         url: /nl/cv/
+  #         weight: 10
+  #       - identifier: projecten
+  #         name: Projecten
+  #         url: /nl/projects/
+  #         weight: 20
+  #       - identifier: contact 
+  #         name: Contact
+  #         url: /nl/contact/
+  #         weight: 30
 
 params:
+  colorScheme: "auto"
+  hidecolorschemetoggle: false
   author: "Pim Nelissen"
-  info: ["Physicist", "Computational Scientist"]
+  info: ["Computational Physics student in Nuclear Science"]
   avatarURL: "images/avatar.jpg"
   since: 2024
   social:
@@ -37,4 +72,8 @@ params:
     - name: "Email"
       icon: "fa-solid fa-envelope fa-2x"
       weight: 3
-      url: "mailto:pi0274ne-s@student.lu.se"
+      url: "mailto:pi0274ne-s@student.lu.se"
+    - name: "Phone"
+      icon: "fa-solid fa-phone fa-2x"
+      weight: 4
+      url: "tel:+46760226691"

content/contact.md

@@ -1,6 +0,0 @@
-+++
-title = "Contact"
-slug = "contact"
-+++
-
-Email: pi0274ne-s@student.lu.se

content/en/contact.md

@@ -0,0 +1,8 @@
++++
+title = "Contact"
+slug = "contact"
++++
+
+Email: pi0274ne-s@student.lu.se
+
+Phone: See my [CV](https://git.pimnel.com/pim/CV/raw/branch/main/cv.pdf).

content/en/projects/bayesian-hierarchical-model.md

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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.

content/en/projects/biome-classification.md

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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. 

content/en/projects/bsc-thesis.md

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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."
+>}}

content/en/projects/entropic-spring.md

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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 $0.3-0.4$." >}}

content/en/projects/first-year-project.md

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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 >}}

content/en/projects/gamma-ray-tracking.md

@@ -0,0 +1,20 @@
++++
+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 today’s nuclear structure studies as well as into the future.

content/en/skills.md

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++++
+title = "Skills"
+slug = "skills"
++++
+
+## 🌟 Core Competencies
+
+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.
+
+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 >}}
+
+---
+
+## 🧪 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>
+  <div class="timeline-item">
+    <div class="dot"></div>
+    <div class="timeline-date">Mar 2025</div>
+    <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>
+<div class="timeline-item">
+    <div class="dot"></div>
+    <div class="timeline-date">Feb 2025</div>
+    <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>

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-  <title>Pim Nelissen</title>
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-<meta property="og:url" content="//localhost:1313/404.html">
-  <meta property="og:site_name" content="Pim Nelissen">
-  <meta property="og:title" content="404 Page not found">
-  <meta property="og:locale" content="en_us">
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-    </a>
-</div>
-
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-  <main class="wrapper">
-    <nav class="navigation">
-  <section class="container">
-    
-    <a class="navigation-title" href="//localhost:1313/">
-      Pim Nelissen
-    </a>
-    
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-  </section>
-</nav>
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-    <div class="content">
-      
-  <section class="container centered">
-  <div class="error">
-    <h1>404</h1>
-    <h2>Page Not Found</h2>
-    <p>Sorry, this page does not exist.<br />You can head back to the <a href="//localhost:1313/">homepage</a>.</p>
-  </div>
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-<!DOCTYPE html>
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-<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
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-  <meta property="og:title" content="Categories">
-  <meta property="og:locale" content="en_us">
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-<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
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-  <main class="wrapper">
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-  <section class="container">
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-      Pim Nelissen
-    </a>
-    
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-      
-  <section class="container taxonomy">
-  <header>
-    <h1 class="title">
-      <a class="title-link" href="//localhost:1313/categories/">Categories</a>
-    </h1>
-  </header>
-  
-  <ul>
-    
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-</section>
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-    </div>
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-    <footer class="footer">
-  <section class="container">
-    ©
-    
-    2025
-    
-    ·
-    
-    Powered by <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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public/categories/index.xml

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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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-    <link>//localhost:1313/categories/</link>
-    <description>Recent content in Categories on Pim Nelissen</description>
-    <generator>Hugo</generator>
-    <language>en-us</language>
-    <atom:link href="//localhost:1313/categories/index.xml" rel="self" type="application/rss+xml" />
-  </channel>
-</rss>

public/fonts/LICENSE.txt

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-Fonticons, Inc. (https://fontawesome.com)
-
---------------------------------------------------------------------------------
-
-Font Awesome Free License
-
-Font Awesome Free is free, open source, and GPL friendly. You can use it for
-commercial projects, open source projects, or really almost whatever you want.
-Full Font Awesome Free license: https://fontawesome.com/license/free.
-
---------------------------------------------------------------------------------
-
-# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
-
-The Font Awesome Free download is licensed under a Creative Commons
-Attribution 4.0 International License and applies to all icons packaged
-as SVG and JS file types.
-
---------------------------------------------------------------------------------
-
-# Fonts: SIL OFL 1.1 License
-
-In the Font Awesome Free download, the SIL OFL license applies to all icons
-packaged as web and desktop font files.
-
-Copyright (c) 2024 Fonticons, Inc. (https://fontawesome.com)
-with Reserved Font Name: "Font Awesome".
-
-This Font Software is licensed under the SIL Open Font License, Version 1.1.
-This license is copied below, and is also available with a FAQ at:
-http://scripts.sil.org/OFL
-
-SIL OPEN FONT LICENSE
-Version 1.1 - 26 February 2007
-
-PREAMBLE
-The goals of the Open Font License (OFL) are to stimulate worldwide
-development of collaborative font projects, to support the font creation
-efforts of academic and linguistic communities, and to provide a free and
-open framework in which fonts may be shared and improved in partnership
-with others.
-
-The OFL allows the licensed fonts to be used, studied, modified and
-redistributed freely as long as they are not sold by themselves. The
-fonts, including any derivative works, can be bundled, embedded,
-redistributed and/or sold with any software provided that any reserved
-names are not used by derivative works. The fonts and derivatives,
-however, cannot be released under any other type of license. The
-requirement for fonts to remain under this license does not apply
-to any document created using the fonts or their derivatives.
-
-DEFINITIONS
-"Font Software" refers to the set of files released by the Copyright
-Holder(s) under this license and clearly marked as such. This may
-include source files, build scripts and documentation.
-
-"Reserved Font Name" refers to any names specified as such after the
-copyright statement(s).
-
-"Original Version" refers to the collection of Font Software components as
-distributed by the Copyright Holder(s).
-
-"Modified Version" refers to any derivative made by adding to, deleting,
-or substituting — in part or in whole — any of the components of the
-Original Version, by changing formats or by porting the Font Software to a
-new environment.
-
-"Author" refers to any designer, engineer, programmer, technical
-writer or other person who contributed to the Font Software.
-
-PERMISSION & CONDITIONS
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of the Font Software, to use, study, copy, merge, embed, modify,
-redistribute, and sell modified and unmodified copies of the Font
-Software, subject to the following conditions:
-
-1) Neither the Font Software nor any of its individual components,
-in Original or Modified Versions, may be sold by itself.
-
-2) Original or Modified Versions of the Font Software may be bundled,
-redistributed and/or sold with any software, provided that each copy
-contains the above copyright notice and this license. These can be
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-3) No Modified Version of the Font Software may use the Reserved Font
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-5) The Font Software, modified or unmodified, in part or in whole,
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-TERMINATION
-This license becomes null and void if any of the above conditions are
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-THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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-
---------------------------------------------------------------------------------
-
-# Code: MIT License (https://opensource.org/licenses/MIT)
-
-In the Font Awesome Free download, the MIT license applies to all non-font and
-non-icon files.
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-Copyright 2024 Fonticons, Inc.
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-Permission is hereby granted, free of charge, to any person obtaining a copy of
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-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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-HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
---------------------------------------------------------------------------------
-
-# Attribution
-
-Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
-Awesome Free files already contain embedded comments with sufficient
-attribution, so you shouldn't need to do anything additional when using these
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-We've kept attribution comments terse, so we ask that you do not actively work
-to remove them from files, especially code. They're a great way for folks to
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---------------------------------------------------------------------------------
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-# Brand Icons
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public/index.html

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-<!DOCTYPE html>
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-<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
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-  <meta property="og:locale" content="en_us">
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-<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="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
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-
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-} else if (body.classList.contains('colorscheme-light') || body.classList.contains('colorscheme-dark')) {
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public/sitemap.xml

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-<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
-  xmlns:xhtml="http://www.w3.org/1999/xhtml">
-  <url>
-    <loc>//localhost:1313/categories/</loc>
-  </url><url>
-    <loc>//localhost:1313/</loc>
-  </url><url>
-    <loc>//localhost:1313/tags/</loc>
-  </url>
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public/tags/index.html

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-  <title>Tags · Pim Nelissen</title>
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-  <meta name="twitter:title" content="Tags">
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-  <meta property="og:site_name" content="Pim Nelissen">
-  <meta property="og:title" content="Tags">
-  <meta property="og:locale" content="en_us">
-  <meta property="og:type" content="website">
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-    <nav class="navigation">
-  <section class="container">
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-    <a class="navigation-title" href="//localhost:1313/">
-      Pim Nelissen
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-  </section>
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-    <div class="content">
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-    </h1>
-  </header>
-  
-  <ul>
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-    </div>
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-    <footer class="footer">
-  <section class="container">
-    ©
-    
-    2025
-    
-    ·
-    
-    Powered by <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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public/tags/index.xml

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-    <atom:link href="//localhost:1313/tags/index.xml" rel="self" type="application/rss+xml" />
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+    border-left: 3px solid var(--fg-color, #999);
+    padding-left: 20px;
+    margin-bottom: 2em;
+    margin-top: 2em;
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+  
+  .timeline-item {
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+    margin-bottom: 3em;
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+  
+  .timeline-item .dot {
+    width: 10px;
+    height: 10px;
+    background-color: var(--fg-color, #999);
+    border-radius: 50%;
+    position: absolute;
+    left: -26px; /* Position the dot on the line */
+    top: 8px;
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+  .timeline-item .timeline-date {
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+    transform: translateX(-140%);
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+    font-weight: normal;
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+  .timeline-content li {
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