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Required: PhD in Bioinformatics, Computational Biology, Computer Science, or equivalent related field Strong programming skills (R and Python required; PyTorch/JAX/TensorFlow preferred) Demonstrated track
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epidemiology. Experience analyzing large cohort, registry, biobank, or omics datasets. Strong programming skills in R, SAS, Stata, or Python. Familiarity with survival analysis, longitudinal data analysis
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- Strong programming skills in R, Python or similar languages - Intrinsic motivation to work on complex, real-world problems with societal relevance Nice to have: - Experience with spatio-temporal
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institutions and/or private companies. Coding capabilities using Python or equivalent languages and/or knowledge of High Energy Resolution Fluorescence Detected-XAS (HERFD-XAS) and X ray Emission Spectroscopy
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evaluation with BER/throughput/bandwidth constraints). Demonstrated experience implementing research software (Python; and/or C/C++) and running simulation studies. Familiarity with modern machine learning
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scripting language (e.g., Python). ● Record of managing and analyzing large-scale genomics or population genetics datasets. ● Demonstrated ability and willingness to work both independently and
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, or (applied) physics. Excellent lab-experiences Advanced mathematical, analytical skills, and programming skills, e.g., in Python. A keen interest in interdisciplinary research. An excellent command of English
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statistical modeling and analysis of human performance data (reaction time, workload, situation awareness), including mixed-effects/multilevel models. Programming proficiency in Python, R, and/or MATLAB
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running data analysis from plant breeding programs. Ensuring compliance with data security standards and compatibility with R-based and/or Python analysis tools and Shiny apps to maintain open-source and
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/computational biology or a related field. Very strong commitment to rigor and scientific integrity. Proficiency in a major programming language used for data analysis, such as R or Python. Experience with single