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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
candidates to develop an innovative and collaborative research program in estuarine and coastal ecology with a strong emphasis on field research and experiential learning. Potential areas of research focus
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base. The Department of Energy and Process Engineering is one of eight departments in the Faculty of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/307684/phd
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responsible related professional research experience. A PhD in a scientific or engineering field may be substituted for up to two (2) years of the required work experience. Any combination of relevant education
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, information visualization and/or human-computer interaction Publications and dissemination of research findings (in journals and at conferences) Active participation in institute’s fund-raising enveadors Active
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area networks (WAN), cloud networks, and other data communication networks. How to Apply: Interested candidates may apply online at https://jobs.coastal.edu/postings/122577 . Applicants must submit a
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and privacy-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and decentralised AI pipelines Formal
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check the minimum entry requirements for the PhD . Applicants must also satisfy Monash’s English Language Proficiency requirements; Demonstrate knowledge of machine learning, medical image analysis
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characterization and aqueous solution geochemistry; 2) statistical methods and machine learning for mixture formulation; 3) environmental impact assessment through LCA. The research envisages the formulation low
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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is seeking a PhD student to join an ongoing Royal Society–Research Ireland University Research Fellowship project focused on Machine Learning for the Design of Additively Manufactured Two-Phase Heat