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position of 1.0 FTE for 30 months or 0.8 FTE for 37 months; access to computational resources (HPC), GIS/data infrastructure, and datasets via collaborative networks; a supportive, interdisciplinary research
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disseminating results through presenting at conferences and writing manuscripts. Additionally, our lab works with an extensive network of world-class collaborators to study other aspects of GPCR signaling
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
, Artificial Intelligence, Neural Networks, Computational Biology, Bioinformatics, Biomedical Informatics or a related field. Programming experience in a language such as Python or R. Experience in writing grant
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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lively, open and critical discussion within and across different fields of research a work environment with close working relationships, networking and social activities a workplace characterised by
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About the Job: You will be responsible for designing and conducting NIH-funded research experiments pertaining to the role of transcription factor mediated networks to direct the fate of stem and iPS
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for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a multidisciplinary team Evidence of strong communication and scientific
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of Department. This postdoctoral position will position you at the core of an international network driving research into ecological change in the world’s northernmost ecosystems, allowing you to
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular