813 postdoc-in-thermal-network-of-the-physical-building Fellowship positions in United States
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facilities at the University of Michigan, including advanced imaging, genomics, and proteomics cores. Support to present your research at national and international conferences and build a professional network
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, nearly 12,000 faculty and staff and 350,000+ living alumni, UC combines a Top 35 public research university with a physical setting The New York Times has called “the most ambitious campus design program
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, data science, differential equations, mathematical logic, mathematical physics, numerical analysis, neural networks, representation theory, and topology, with potential interdisciplinary applications
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high-potential research within the excellence-driven environment of CMU. Offering benefits beyond ‘traditional’ postdoc roles, Fellows are provided with the resources, flexibility, and environment
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expertise in at least one (or more) of the following areas: Computer Architecture/Systems: Design, modeling, simulation, and/or physical design of heterogeneous system/processor architectures; Silicon
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condition through world-class teaching, research, and service. With a robust benefits package, collaborative atmosphere, and focus on work-life balance, CSU is where you can thrive, grow, and make a lasting
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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to the logistics of community-engaged action-research, including ethics review, financial reporting, grant preparation, etc.; (4) consolidating a relevant network of community-engaged researchers and Indigenous
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-management practices and systems (e.g., PastPerfect or similar). Essential Responsibilities Process acquisitions and loans (incoming and outgoing): cataloging, condition reporting, packing/unpacking, rehousing
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works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include