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or a related field; strong academic results and foundations in machine learning, linear algebra, probability and optimisation; and strong Python and PyTorch (or comparable framework) skills. Only
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific computing environment. A fundamental
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-driven modelling. Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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learning, computing, data science, biomedical engineering, or a related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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experimental data. • Proficiency in scientific programming and data analysis tools (e.g., Python, R, Linux/Unix environments). • Demonstrated track record of publishing scientific results in peer-reviewed
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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
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other omics data. Programming experience in R and/or Python, as well as familiarity with Unix/Linux-based computational environments and reproducible research workflows. Interest in integrating genetic