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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Li’s lab (https://jililab.org) at the University of North Carolina at Chapel Hill is recruiting postdoctoral researchers to join his interdisciplinary team. The lab focuses on integrative cancer omics
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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a grant from the Swiss National Science Foundation entitled Machine Learning for Optimized Ab-initio Quantum Transport Simulations (MALOQ). It officially started on January 1st 2026 and will
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aims to compare the optimality of the various quantum network setups and protocols, estimate their cost, and design more optimal solutions if needed. The candidate as the key member of the DIFFQRN
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(EU NIS-2 Directive), e.g., compliance optimization and automatization. The postdoctoral researchers will belong to the graduate school within the Wallenberg AI, Autonomous Systems and Software Program
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using AI. Major equipment in the lab includes a two-photon mesoscope optimized for large field-of-view calcium imaging and simultaneous two-photon optogenetics, 16 custom-built training rigs for virtual
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and predictive confidence, including sensitivity and identifiability analyses Compare grey-box models against purely mechanistic and purely data-driven approaches Optimize model performance
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synthesis. Our state-of-the-art analytical instruments help us to quickly screen and optimize electrochemical reactions. In your role, you will join the electrochemistry subgroup of the Giovannitti group
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benefits from the collaboration with several universities and research centres worldwide, most often with the closer universities of Genova and Verona. AIGO is part of ELLIS (https://ellis.eu
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Engineering, and related fields, with a view to develop and optimize 3D bioprinted models, cell selection and expansion for bone models, biological and mechanical characterization of bioprinted tissues