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multi-agent pathfinding (MAPF) algorithms - Experience across multiple areas is a strong plus; Experience developing ML-based optimization approaches is a strong plus; A strong publication track record is
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the United States, and around the globe. The GHRC supports and advances multi-disciplinary large animal and insect vector research, education, and training opportunities for faculty at Texas A&M and their partners
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
the assessment of risks pertaining to infectious agents and biological toxins; reviews research proposals to protect environment, health and safety; trains laboratory personnel in biosafety principles; addresses
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Jobs from This Employer https://main.hercjobs.org/jobs/21796038/front-desk-agent Return to Search Results
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to manage workflows, resolve conflicts, and support agents in a fast-paced environment. Preferred Qualifications Experience supporting or managing within a multi-tiered service center model. Familiarity with
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workflows, resolve conflicts, and support agents in a fast-paced environment. Preferred Qualifications Experience supporting or managing within a multi-tiered service center model. Familiarity with HCM
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on developing new stable organic radical polarizing agents for nuclear spin hyperpolarization in NMR spectroscopy and imaging applications. Project Overview: This research aims to leverage radical dynamics
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around the globe. The GHRC supports and advances multi-disciplinary large animal and insect vector research, education, and training opportunities for faculty at Texas A&M and their partners from other
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interpretable AI. We also welcome candidates with interests in emerging areas such as multi-agent learning and the development of foundation models for electronic health records (EHRs). Strong skills in algorithm
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, and clinical data. Empty heading Core responsibilities Design, train and deploy multi-modal foundation models for single-cell and spatial cancer data Build scalable training pipelines in PyTorch/JAX