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, bioinformatics, aging biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work in applications
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adaptive robotic strategies. The work will involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models
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members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans at Brookhaven National Laboratory . Equal
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of quantum computing, quantum algorithms and complexity, quantum cryptography, quantum program verification, quantum machine learning, etc. * Within the predetermined research scope and methodology, conduct
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conferences. Engage in community knowledge-sharing (e.g. tutorials for the NERSC user base). What is Required: PhD awarded within the last five years in Physics, Computational Chemistry, Computational Science
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collaboration. Qualifications: Applicants must have a PhD in Robotics, Control Engineering, Machine Learning, AI, Mechanical or Electrical Engineering, or a closely related field. Strong focus on robot
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-reviewed venues and conferences. Engage in community knowledge-sharing (e.g. tutorials for the NERSC user base). What is Required: PhD awarded within the last five years in Physics, Computational Chemistry
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human