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of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced
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structure-preserving, machine learning–accelerated scientific computing for plasma physics applications. In particular, the project involves developing data-driven collisional kinetic models and numerical
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, electron microscopy, material testing, and physical simulation machine (Gleeble). Research on AM is conducted mainly experimentally and through modelling and simulation to understand, develop, and improve
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific vision
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent
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), Computational Biology (stochastic and analytical models of gene expression), Signal Processing (machine learning, image and signal processing), Biophysics, Microbiology and Single-cell Biology (flow cytometry
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and growing scientific team, with opportunities to shape the direction of an exciting research program at the intersection of gene regulation, stem cell biology, and machine learning. This is an
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and