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Postdoctoral Fellow in Neutrino Astrophysics Position in the School of Physics - Otte Research Group
performance requirements of Trinity One. In addition, the candidate is expected to develop a strong and visible research program that connects Trinity’s neutrino observations with the broader multi-messenger
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. • Experience with data analysis using statistical inference techniques. • Experience with health economic evaluations. • Experience with parallel and/or high-performance computing. • Familiarity with agent-based
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ensuring access to primary samples from a diverse, nation-wide pool of patients. Our high-throughput science is enabled by state-of-the-art facilities for next-generation sequencing and high-performance
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positions in a one-year Cardiovascular Fellowship program. This position is open to Advanced Practice Providers (Physician Assistants and Nurse Practitioners) and will provide a faculty appointment at the
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UnitedKingdomTime) Position Description: Apply Position Description Position Summary The SCALES (Scholars for Climate and Advanced Leadership in Environment and Sustainability) Postdoctoral Fellows Program at Duke
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) Application Deadline 6 Apr 2026 - 13:00 (UTC) Country Finland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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for the Pitkow Lab. Core Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments
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from AI data centres and high-performance computing. The appointed Research Associate/Fellow will conduct computational studies of electronic transport and quantum phenomena in 2D semiconductor materials
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immunophenotyping of colonic tissues. Develop and optimize small animal models of colitis to investigate the mechanistic role of SCFA-producing microbes in mucosal repair. Perform data analysis using computational
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may include—but are not limited to—AI-based grid operation and planning, reinforcement learning for distributed system coordination, electricity market design, pricing mechanisms for reliability and