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or related field. Expertise in graph machine learning and demonstrated experience in multi-omics data integration. Strong programming skills in Python and its scientific and graph ML libraries (numpy, pandas
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. The applicants must demonstrate practical experience with heat transfer simulations and materials testing. (Please shortly describe concrete examples of your own relevant previous work involving these topics
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. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development of numerical implementations
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of experience with research on fish diseases, and a large international network. The section is the National reference laboratory on fish, crustacean and mollusc diseases, and EU reference laboratory on fish- and
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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. Crouzier brings extensive experience in mucin research and technology translation, having successfully founded two biotech startups focused on mucin technologies and planning further entrepreneurial
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on nutrient intake by plant cells. The applicants must demonstrate practical experience with surface functionalization and knowledge of interactions between nanoparticles and living systems. (Please shortly
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chemistry background, extensive experience working with spectroscopy (NMR and optical), chromatographic analysis (HPLC, LC-MS), mass spectrometry and/or organic synthesis is essential, and knowledge within
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related field Experience with Earth System or biogeochemical modelling Strong programming skills (e.g., Python, R, MATLAB) and experience with advanced machine learning modelling A strong interest in
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, you must hold a PhD degree in analytical chemistry, chemical engineering, biomedical engineering or other related disciplines. Experience in SERS and SERS data analysis. Experience in sample preparation