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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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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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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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, 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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, 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
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, adhering to a Design-Build-Test-Learn methodology for integrated research. Within the Schwender group, the primary objective is to assess the outcomes of metabolic engineering endeavors aimed at enhancing
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spectroscopic or scattering techniques such as x-ray scattering, neutron scattering, photoelectron spectroscopy, optical spectroscopy, etc. Familiarity with, or motivation to learn, facility proposal submission