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with biological datasets; experience with R, Python, or omics analysis pipelines is advantageous but a keen willingness to learn is equally valued Language Requirements: Applicants must demonstrate
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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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synucleinopathies Familiarity with multi-omics approaches, e.g., snRNA-seq Experience with image quantification software and/or basic data analysis in R or Python Experience working in interdisciplinary and
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, computational biology, bioinformatics, data science, or related fields Strong interest in clinical and biomedical data, translational research, and health informatics Experience with programming skills in Python
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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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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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++ 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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programming skills in C++ and/or Python; experience with high-performance or real-time computing, e.g. GPU, multi-core, embedded, is desirable Prior experience with SOFA is a clear advantage; experience with
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contribute to the development of a proof of concept obtained at University Côte d’Azur for accessing the content of a metabolomics knowledge graph (KG) with a large language model. It is Python prototype of a