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to learn: R, python, Java, web services, Docker containers and UI design Git-based subversion control (GitHub, GitLab) Non-target workflows for mass spectrometry data Major cheminformatics toolkits (e.g. CDK
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simulation (Pytorch, Python, MATLAB, ...). A general interest in health and biology is welcome. Practical information MORPHEME research team is a joint research group between INRIA Sophia Antipolis
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using the Kokkos library, for which there is strong expertise at MdS and within the CExA [1]project. Subsequently, a Python interface will be established to make the code easily usable by the community
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(Python, Pytorch, Matlab...). A general interest in health and biology is welcome. Practical information The post-doc will take place within the Inria Morpheme team, a joint research group between Inria
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auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application Application files should contain a resumé, an application letter and grade records of the 2 last
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. Required Skills and Candidate Profile The project is intended for a candidate with: ➢ Skills in medical image processing and deep learning adapted to clinical applications. ➢ A good knowledge of Python
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, clustering, classification • deep learning, variational auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application: Application files should contain a resumé, an
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. Experience or good knowledge of analyzing other types of biological data. Knowledge of Python. Experience in leading research projects. Other expected qualities: Teamwork skills. Strong collaboration skills
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background in one of the following areas: Statistical Physics Applied Mathematics Statistics & Bayesian Inference Proficiency in Python is also expected. Contacts dbc-epi-recrutement at pasteur dot fr
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should be proficient in Python, PyTorch or Scikit-learn. The candidate should also be endowed with a strong passion for multidisciplinary studies and all aspects of research ranging from fundamental work