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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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professional life 30 days of vacation, annual bonus payment in accordance with TVöD Bund (German federal civil service collective agreement), and company pension plan (VBL) Discounted company or Germany ticket
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fields. Strong programming skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. Interest in Speech/Language Processing and its application. Know-how
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(bulk and single-cell) Proficiency in relevant programming languages (e.g. Python, R, etc.), HPC environments (e.g. Slurm), and workflow management systems (e.g. Snakemake) To work accurately and being
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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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Excellent command of advanced statistical methods, such as network analyses, using the R programming language Strong publication record relative to the career stage, demonstrating research excellence and
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are currently developing some mathematical software to aid in our computations with cluster algebras. Some programming familiarity and experience with mathematical software like Sage or Mathematica or even
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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
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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- Knowledge of quantitative data collection and analysis - Knowledge of French - Willingness to learn Phyton as a programming language - Experience in analyzing non-verbal communication - Specialist knowledge