30 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" research jobs at Leibniz in Germany
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programming, ideally also in C or C++ (the language of the LPJ-GUESS model) Documented ability to publish in high/quality scientific journals Very good communication skills in English We Offer Access
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immediately) Supervisor(s): Prof. Tilman Grune / Prof. Christian Stoppe Enrolment in a Doctoral Program: German Institute of Human Nutrition / University of Potsdam Project description: This project aims
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) and related to the major multi-national initiative “GOE-DEEP” supported by the International Continental Scientific Drilling Program (ICDP) and aiming to study the climate at the time of the first rise
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, environmental and/or ecological sciences ability to work with large environmental and ecological data sets, incl. empirical, remote-sensing and simulated model data knowledge in programming languages (UNIX, C
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The LIT - Leibniz Institute for Immunotherapy (foundation under civil law) (https://lit.eu/ ) – is a biomedical research centre focusing on translational immunology in the fields of cancer
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remuneration in accordance with TV-L provisions based on personal qualifications, with an annual bonus, capital-forming benefits, and a company pension plan (VBL) a contribution toward a job ticket a family
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programming skills in Python Experience with scientific Python tools such as pandas, scikit-learn, matplotlib, and Jupyter; experience with PyTorch is a plus Familiarity with microbiome data analysis
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– company pension plan Senckenberg is committed to diversity. We benefit from the different expertise, perspectives and personalities of our staff and welcome every application from qualified candidates
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of nutrition and health, with the aim of understanding the molecular basis of nutrition-dependent diseases, and of developing new strategies for treatment and prevention (https://www.dife.de/en/). We invite
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learning and deep learning Excellent programming skills in Python Practical experience with PyTorch (preferred) and/or with TensorFlow, scikit-learn, and GitHub Experience with scientific experimentation and