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Field
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, machine learning, governance and operations, we aim to address the complex challenges and opportunities of space exploration. CfAI is a large research group in the Department of Physics at Durham University
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, experimentalists, and engineers/physicists conducting basic and applied research in support of the Laboratory’s missions. Participate in the development of multi-physics simulations with machine learning (ML
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
The ideal candidate would have some, but not necessarily all, knowledge of and skills in: Familiarity with artificial intelligence, machine learning, physics-informed neural networks, or reduced-order
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within the Texas Children's Cancer Center. The project aims to develop and test novel CAR-redirected immunotherapy for pediatric solid tumors. In particular, gene knockout and knock-in screens will be
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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. Teamwork: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data