10 learning-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at Virginia Tech in United States
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-learning methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems
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that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning approaches to identify FDA
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ethic, and enthusiasm for learning as much as prior technical expertise. Note: Candidates with complementary training backgrounds are encouraged to apply if they bring strong motivation to learn new
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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. • Possess molecular virology or protein biology skills. • Should be enthusiastic and dedicated, have the ability and interest in learning new techniques; must be able to follow verbal and written instructions
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. deep learning. Experience with at least two of the following: remote sensing of surface and ground water resources, analysis of satellite gravimetry (GRACE) data, analysis of radar and optical remote
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with experts in AI and machine learning. The post-doctoral associate will also be responsible for data management and dissemination of scientific results. The successful candidate will join a
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in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending
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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical