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-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), Quantitative proteomics workflows, Data normalization and quality control strategies, Strong experience in processing and interpreting
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, urban analytics, geoinformatics, or a related field; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python
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assessment. Programming and data-analysis skills in a reproducible scientific workflow, for example using Python or comparable tools, and experience with GIS-based spatial analysis. The ability to work
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). Experience in multi-modal data integration for prioritizing disease-specific targets: combining quantitative multi-omics approaches (es. RNA-seq, Ribo-seq, proteomics, immunopeptidomics). Strong programming
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transformers, self-supervised learning, foundation models, autoencoders or related architectures; • strong programming skills in Python and experience with a deep-learning framework such as PyTorch