Sort by
Refine Your Search
-
-driven modelling. Experience with numerical modelling, simulation, optimization, control, or engineering-data analysis. Good programming skills in Python, MATLAB/Simulink, or a comparable scientific
-
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
-
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
-
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
-
competencies Applicants must hold an MSc degree in statistics, genetic epidemiology, bioinformatics, clinical data science, medicine, or a related field. Programming skills (e.g., R, Python, or similar) and
-
representation learning. Programming skills (e.g., Python) and experience with deep learning frameworks (e.g. PyTorch) Interest in applications to ecological or biological networks Good analytical and
-
package is not required, but experience with one or more computational environments, such as MATLAB, Python or similar tools, will support your work in the project. Knowledge of Power-to-X, electrolysis
-
analysis. You master programming in for example Python, MATLAB, or a similar platform and you are motivated to further develop your skills in scientific computing and hyperspectral data processing. You have
-
methods (e.g., coding in Python/R, working with APIs, scraping data, building or applying models). • Can bridge theoretical insight with concrete technical implementation and empirical analysis. • Is
-
programming (particularly Python) and modern machine learning frameworks (e.g., PyTorch, TensorFlow). An interest in AI security, trustworthy ML, or the reliability of ML systems in safety-critical settings