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Field
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statistical and mathematical modelling of infectious diseases. You have experience with scientific programming (e.g. in R, Python) If you have experience with the research topic, this is a plus. You have
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Programming skills (commonly C++, Python, or similar languages used in HEP frameworks), Monte Carlo simulation methods and data analysis techniques Interest in connecting ideas and people across disciplines
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Thermodynamics • Ability to program in Matlab or Python The following skills are desirable but not essential: • Experience in numerical methods (e.g. finite elements) Application Procedure Informal enquiries
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laboratory work (DNA extraction, PCR, sequencing workflows) Bioinformatics and statistical analysis (e.g., R or Python) Biodiversity assessment or ecological community analysis Fieldwork experience in
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related discipline. A solid background in de novo protein design, protein structure prediction (Rosetta, AlphaFold, …), protein expression, structure elucidation, machine learning, C/C++ and/or Python with
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. The candidate should ideally have experience or interest in one or several of the following areas: A solid foundation in programming and system development, particularly using Python and machine learning
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modeling Programming skills in Python and machine learning packages such as PyTorch and TensorFlow Scientific curiosity and critical thinking Ability to work in interdisciplinary environments Motivation
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Engineering, Physics or similar is required Knowledge of at least one programming language is expected: Python, Matlab, Fortran, C++, … Experience and/or keen interest in transport modeling of porous systems
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statistical processing (e.g. R, Python) Knowledge of atmospheric chemistry and/or air quality would be an asset Ability to work independently, scientific rigor, adaptability, teamwork skills, open‑mindedness
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geophysics, physics, geoscience, computational geoscience, or related natural sciences with an overall grade of at least good Experience in programming (e.g., MATLAB, Python, C/C++) Advanced knowledge