345 machine-learning "https:" "https:" "https:" Postdoctoral positions in United States
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- Zintellect
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Field
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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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Join the HJF Team! HJF is seeking Postdoctoral Fellow - Computational Biology & Machine Learning who will be responsible for initiating and carrying out research projects in collaboration with a
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non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar. Responsibilities
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postdoctoral researcher will conduct cutting-edge research in areas such as cyber-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will
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conduct collaborative research at the intersection of non-canonical kinetic theory, non-conventional plasma turbulence modeling, high-speed flows, scientific machine learning for multi-physics problems
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quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale
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, data science using EHRs, machine learning, data mining, and natural language processing are preferred. Job Description: Develop large language models and other methods and tools to effectively use
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About the Opportunity Job Summary: The Lopez Group at Northeastern University is seeking a postdoctoral researcher with experience in machine learning and organic chemistry to join a collaborative
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machine learning group on database set up CO2 removal process Job Requirements: Candidates with strong background in Materials chemistry/Physical chemistry Experience with MOF synthesis, energy storage
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families of algorithms for cryo-electron microscopy (cryo-EM) and broader applications in data science * Protein structure prediction: Combining traditional and machine learning approaches to protein