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in programming, ideally with experience in languages commonly used in computational biology (e.g. Python, C). Experience with HPC, workflow managers (e.g. Snakemake, Nextflow), and containerization
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Strong background in differential equations and numerical methods Solid programming skills e.g. Python, C++, Julia or similar Interest in interdisciplinary research bridging mathematics and environmental
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novel machine-learning methodologies Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices Experience training and deploying machine-learning
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of molecular phenotypes, Mendelian Randomization, co-localisation); Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R; Strong quantitative skills and
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Familiarity with omics approaches such as scRNA-seq, proteomics Programming or data analysis skills such as R, Python, or similar Experience in international collaborative projects Experience with laboratory
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experience in writing successful research proposals Experience in research project management or coordination Solid software development skills in Python; C/C++ is a plus Fluency in English is required
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for engineers Good knowledge of experimental techniques and control engineering Strong programming skills, particularly in Python and Fortran 90 Knowledge of fracture mechanics is an asset Excellent command
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++ and/or Python Experience with SOFA, FEniCSx or similar simulation frameworks is a strong plus Motivation to work at the crossroads of mechanics, AI and medical technology, in close collaboration with
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, or a closely related field Strong programming skills, e.g., Python, and familiarity with machine learning and/or software engineering workflows; experience with Git and empirical evaluation Experience
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, systems biology, biomedical data science, cancer biology, or a related field Strong experience in omics data analysis, ideally including single-cell or spatial transcriptomics Proficiency in R and/or Python