736 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs in United States
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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
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at finale.seas.harvard.edu and our group’s webpage https://dtak.github.io/ We work on probabilistic models, reinforcement learning, and interpretability + human factors. Basic Qualifications Candidates are required to have
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projects, as well as in multiagent systems, including computational game theory, security games, machine learning in multiagent settings, automated planning under uncertainty, social networks and others
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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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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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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual