56 electric-machine-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at Rutgers University in United States
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Position Details Position Information Recruitment/Posting Title PostDoctoral Associate in Artificial Intelligence/Machine Learning and Data Science Department Quantitative Biomedicine Inst Salary
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settings, as well as machine learning and multivariate decoding of neuroimaging data to predict subjective experiences and individual differences. Successful candidates will be supported in building
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of small-molecule drugs, nanomaterials/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning
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theoretical chemistry, machine learning and quantum information science. Candidates with experience in method development and high-performance computing are especially encouraged to apply. A Ph.D. in
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) Comparative Morphology and Machine Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb
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) Comparative Morphology and Machine Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb
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development of advanced sensor systems, from the physical fabrication of flexible printed circuit boards (FPCBs) to the implementation of machine learning algorithms for real-time signal analysis. The
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, Movebank), remote sensing data, GIS tools and quantitative/spatial approaches including machine learning to understand how wildlife species are reacting to climate change at continental or global
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record of peer-reviewed publications Experience developing software with Python, R, and/or Julia Experience with machine learning and AI algorithms and tooling (e.g. PyTorch) Experience with remote
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of small-molecule drugs, nanomaterials/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning