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april 2026 update

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Pim Nelissen
2026-04-03 14:42:23 +02:00
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content/en/skills.md
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@ -9,7 +9,7 @@ From my background in physics and computational science, I have a strong foundat
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.
I am currently working on my masters thesis. In the first half of my masters thesis I have developed PG-RAD, a Python simulation tool for generating realistic mobile gamma spectrometry data that mimics real-world data. It is particularly useful for simulating localization scenarios of radioactive point sources using car-borne spectrometry setups. In the second half of my thesis, I am using the generated data to quickly generate various scenarios, in order to test Bayesian source localization algorithms for their effectiveness at localizing sources as a function of source activity, source distance from the road, and more.
I am currently working on my masters thesis. In the first half of my masters thesis I have developed [PG-RAD](http://github.com/pim-n/pg-rad), a Python simulation tool for generating realistic mobile gamma spectrometry data that mimics real-world data. It is particularly useful for simulating localization scenarios of radioactive point sources using car-borne spectrometry setups. In the second half of my thesis, I am using the generated data to quickly generate various scenarios, in order to test Bayesian source localization algorithms for their effectiveness at localizing sources as a function of source activity, source distance from the road, and more.
{{< notice tip >}}A selection of code can be found on my [git instance](http://git.pimnel.com/pim).{{< /notice >}}