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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 19 days ago
the University of North Carolina at Chapel Hill. These appointments will be in the Institute's Center for Environmental Modeling and Policy Development (IE-CEMPD). The selected candidates will work with Dr. Sarav
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collaboration with scientists at the Snow Centre for Immune Health based at The Walter and Eliza Hall Institute in Melbourne, Australia. You will be part of a multi-disciplinary team that has an ambitious long
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Club Centre for Suicide Research and Prevention (30002) Categories: Senior Research Staff & Post-doctoral Fellow Post-doctoral Fellow in the Hong Kong Jockey Club Centre for Suicide Research and
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Knowledge with multi-modal masked auto encoders Expertise in developing larger-scale AI models (SLURM) Other Information: Position is location onsite in Upton, NY BNL policy requires that after obtaining a
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, etc.) Experience working with probabilistic databases, provenance, or uncertain data Exposure to decision-making, multi-objective optimization, or declarative problem modeling Prior involvement in open
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable predictive models for complex multi-physics dynamical
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Post Doctoral Fellow - Biostatistics and Data Science - (2500018O) POSTDOCTORAL FELLOW: The Maryland Psychiatric Research Center (MPRC) at the University of Maryland School of Medicine is
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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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tissue using genetic mouse models, functional screens, and single cell and spatial omics technologies. More details about the laboratory: https://www.ibt.cas.cz/en/research-laboratories/laboratory
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clinical data to better characterize disease processes. ● Clinical and multi-omic data fusion: Build machine learning pipelines that integrate electronic medical record data, genomics (animal and microbial