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post-doctoral research associate position in machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse
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Apply Now Job ID JR101405Date posted 09/12/2024 The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis of the big data acquired by electron microscopy. • You work
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analysis of atmospheric numerical model output (e.g., WRF, PALM, SAM) Experience with machine learning and artificial intelligence techniques Experience with predictive modeling Environmental, Health
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that incorporate latest machine-learning algorithms). Furthermore, the successful candidate will collaborate broadly with the other members of IO and CFN, leveraging their expertise in design and fabrication
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advancing the development of machine-learned interatomic potentials (MLIPs) for nanomaterials and applying them to model complex systems. Your work will involve close collaboration with scientists
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, and Abilities: Experience in NGS library preparation and data analyses. Bioinformatic/programming skills (MatLab, Python, R, etc). Experience in application of Artificial Intelligence/Machine Learning
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of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events. Candidates must havereceived a PhD
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses