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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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enable physics-informed AI agents that accelerate discovery in catalysis science. The successful candidate will focus on the development and application of machine learning and AI approaches to analyze
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and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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physics. This will include training and deploying machine-learning models that can recognise and classify geometric features critical to simulation accuracy, enabling the removal or simplification
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, AND ABILITIES REQUIRED: Excellent interpersonal skills, dependable, motivated and willingness to learn new tasks Computer skills, including prior use of Microsoft Office products Ability to relate
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for physical AI and physics‑based simulation for complex mechanical systems. We are now seeking a postdoctoral researcher for a project aiming at physics‑informed autonomous control of machines operating in
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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human health. Within this mission, the Iorio Group works at the intersection of computational biology, functional genomics, and precision oncology, integrating machine learning, large-scale CRISPR
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resonance imaging data among other imaging modalities, machine learning methods for prediction, treatment effect estimation, and contribute to understanding brain biomarkers of Alzheimer’s disease and their