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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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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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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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machine learning models to enhance materials research. You will model the stability and transformations of nanomaterials under different conditions. You will disseminate your results through publications
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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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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