2,409 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" Postdoctoral research jobs in United States
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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artificial intelligence and machine learning Strong analytical and problem-solving skills Experience working with large administrative or clinical datasets Proven ability to work both independently and
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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advanced, independent, or collaborative research, aiming for high-impact publications of scholarly merit. Simultaneously, the researcher will teach up to three courses per year, distributed over three
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: The appointment will be for one year. Appointment Start Date: 01/15/2026 or once the position is filled Group or Departmental Website: https://woods.stanford.edu/ (link is external) https://fieldlab.stanford.edu
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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning
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written), time management, project management, and presentation skills Proficient with computer applications and programs associated with the position (i.e. Microsoft Office suite) Strong attention
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Position Description The School of Physics (https://physics.gatech.edu/ ) at the Georgia Institute of Technology in Atlanta, Georgia invites applications for a Postdoctoral Fellow position in Artificial
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-time work on the proposed historical project during the period of the appointment and will not have teaching or administrative responsibilities. Those hired at more senior ranks may be asked to teach
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights