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to genetic, genomic, and clinical data analysis are strongly encouraged to apply. Proficiency in programming languages such as Python or R (C/C++ is a plus). Prior experience working with genomic and other
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in Europe by fueling innovation through research partnerships with industry, boosting R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers from
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., PyTorch, JAX, TensorFlow, R, Bash). Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, Nature Methods, Nature Biotech). Background in at least one of the following: generative modeling
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desirable. Basic computational analysis skills (e.g., R or Python for sequencing data) are a plus but not required. Candidates must be team-oriented, organized, and enthusiastic about collaborative science
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model simulation techniques. Can conduct data analysis and visualize results in R or comparable tools. Has knowledge of environmental risk assessment of pesticides on non-target organisms. Possesses basic
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, Berkeley Madonna, and Microsoft Office. Experience with statistical data analysis and related packages, such as SPSS and R. Skills Highly motivated Fluent in English Organizational skills Manuscript writing
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, parameterization, simulation experiments, and output analysis very good programming skills in at least one language (e. g., R, Python, C++, or Julia) a strong interest in interdisciplinary work (e. g., demonstrated
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. Programming experience is required (Python, R). Strong analytical, organizational, and record-keeping skills Interest in working in a multidisciplinary and multicultural team Willing to collaborate with
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. Strong background in neuroimaging techniques (e.g., MRI, PET) and computational modeling. Proficiency in programming languages such as Python, MATLAB, or R. Experience with image processing tools and
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, computational data analysis and microbial evolution, with a solid understanding of quantitative statistics and programming (Python, R, MATLAB, etc.). Additional expertise in ancient DNA, (bio)archaeology