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, planning) in the co-design process and run experiments across classes of environments (e.g., forests, industrial facilities, underwater caves) and missions (e.g., exploration, object search and recovery
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addiction susceptibility by studying a naturally occurring mutation (R181C) discovered in the Norwegian popu-lation. This mutation abolishes opioid receptor signaling and offers unprecedented insights
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upcoming SPHEREx data. The candidate will perform all levels of data analysis, from the processing of raw data to maps to power spectrum estimation of resulting CIB maps. The candidate will also have the
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Life and Health Sciences Research Institute (ICVS), from the School of Medicine (EM) of the University of Minho | Portugal | about 1 month ago
Technology, the Portugal 2030 framework program, and the operational program Innovation and Digital Transition Program - Compete 2030, under the following conditions: Scientific Area: Basic Medicine
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Regional Development Fund (ERDF) via the COMPETE2030 - PITD (Innovation and Digital Transition Programme), under the following conditions: Scientific Area: Chemical and Biological Engineering, Biotechnology
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adaptations, digital, technical, or physical adjustments, or other practical measures. If you have an immigrant background, a disability, or CV gaps (Norwegian), we encourage you to indicate this in the job
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the final exam. Desired qualifications: Background in probability theory and Monte Carlo methods, and familiarity with stochastic processes. Strong background in hydrodynamic, nonlinear and stochastic wave
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qualifications: A strong background in algebraic topology. Experience in any of the following: Higher category theory, stable homotopy theory, simplicial methods. An interest in computer algebra and programming
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like Glioblastoma. In this context, the present PhD Research Fellow will contribute to the project using semiconductor processing and quantum technology. The PhD Research Fellow will be affiliated with
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML