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Field
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in the United States. Preferred methodological skills include statistical analysis of survey and other large-n data, qualitative interviews, and/or text analysis and machine learning skills
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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generation Developing and optimizing generative models for de novo minibinder design Integrating structural biology data into AI pipelines for receptor–ligand interaction modeling Fine-tuning large protein
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reconstruction Background in transport modeling (continuum methods, CFD, or data‑driven surrogates) Interest in or experience with machine learning/AI applied to scientific data Demonstrated research productivity
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to the topic, including food safety, microbiology, computational biology, machine learning, artificial intelligence, data science, or other related scientific fields. Familiarity with data-driven
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this diversity. Our research spans comparative genomics, chromatin architecture, gene expression, protein abundance, and metabolite profiling—combined with computational biology, machine learning, and advanced
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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across large cohorts (e.g., >300 samples) demonstrated experience in data analysis, visualisation, and machine learning, using programming languages such as R or Python excellent interpersonal, verbal, and
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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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Geosciences, Environmental sciences, Civil or Environmental Engineering, Physics or Mathematics or a related discipline Experience in programming (e.g., Python, MATLAB, or similar), interest in machine learning