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atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible computational workflows, collaborate with
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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; experience with foundational AI model development/fine-tuning and machine learning and/or deep learning; strong programming skills (e.g., Python, JavaScript, PostgreSQL) with clear expertise in front-end and
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machine learning, with demonstrated experience in developing and training neural networks for predictive modeling. Position Requirements: The successful candidate is expected to: Build and evaluate chemical
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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infrastructure that will decrease energy use, support improved learning environments, improve indoor air quality, and strengthen classroom environments with reliable energy solutions. To date, through competitive
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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. The ideal candidate should have a strong background in artificial intelligence and machine learning, with demonstrated experience in developing and training neural networks for predictive modeling. Position
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-career scientist to develop cutting-edge machine learning approaches for understanding and designing pathogen antigens. This is a unique opportunity to help shape a new research program at the intersection
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therapies, and machine learning and artificial intelligence. Postdoctoral fellows will join a highly collaborative research environment at Rice, with access to a large and growing synthetic biology community