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critical role in advancing computational materials science by developing and applying first-principles and machine learning methods, with a focus on interatomic potential development and large-scale
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, genomic datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding
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interdisciplinary team at the NSF COMPASS Center, which integrates tissue engineering, stem cells, materials, virology, computational biology, machine learning, molecular environmental engineering, science
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, the postdoctoral associate will help to coordinate evidence-based learning networks of NPS units and other educational organizations to (1) direct data collection and conduct research on their programs and
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foresee pandemics and minimize their impact. The Center invites applications for a Postdoctoral Associate with expertise in machine learning and computational biology. This position focuses
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and networking, with a particular focus on 5G and Beyond 5G technologies and applications of artificial intelligence and machine learning to wireless systems. The candidate will oversee the design and
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to identify FDA approved drugs that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning
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interest relate to Learning from Landscapes, a modeling framework that leverages interpretable machine learning to understand how landscape connectivity influences water quality. The selected candidate will
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(National Ecological Observatory Network), and/or GLEON (Global Lake Ecological Observatory Network) and a suite of process and machine learning models; coupling catchment and lake models to build new
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(tensor/sparse grid based), reduced order modeling, digital twins and scientific machine learning. The application areas involve kinetic systems in plasma, quantum computing, wave equations and control, etc