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candidates will have strong past experience in scientific machine learning. Experiences in large language models, molecular modeling and simulations, and/or polymer science are preferred but not required
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-Phenomenology (hep-ph) , High Energy Physics , High Energy Theory , Machine Learning , Neutrino physics , Particle , Particle Physics , Particle Theory , QCD , Theoretical high energy physics , Theoretical High
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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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opportunities for methodological and/or software development, as well as the integration of machine learning into the project, depending on the candidate’s interests. This position is one of three Postdoctoral
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Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine Learning, addressing challenges
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The postdoctoral researcher will lead the development of computational methods for aligning cortical organisation across species using transcriptomic and anatomical data combined with modern machine
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable prediction of gene perturbation
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are expected to have a Ph.D. in theoretical particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning
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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong