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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data
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chromatography. The candidate will prepare samples for immunopeptidomics by liquid chromatography coupled with mass spectrometry tandem (LC-MS/MS), analyze large datasets, and develop scripts and machine learning
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scientific programming language (Python, Fortran, or C); familiarity with inversion methods or machine learning; experience with high-performance computing (HPC) environments. Desirable requirements Experience
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