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platforms a plus Experience applying machine learning, artificial intelligence, and large language models to research a plus The anticipated start date is September 1, 2026. The postdoctoral position incoming
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. The work location for this position is in-person on campus at Princeton University. This position is subject to the University's background check policy. Applicants must apply online at https
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 23 days ago
professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly skilled workforce
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(i) strong foundations in digital communications and statistical signal processing with (ii) hands-on implementation using reinforcement learning (including multi-agent RL), large language models (LLMs
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opportunity to collaborate and learn from experts researching, developing, and testing emerging technologies in marine energy and/or ocean-based industries. You will conduct research at both your academic
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of programming and implementing both back- and front-end solutions, using (open source) APIs. Evidence of being able to acquire additional funding (projects/scholarships) is a strong plus. You are willing to work
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Department invites applications for postdoctoral and more senior research positions in two areas of computer science. 1) Quantum computing and quantum architecture and systems. 2) Reinforcement learning
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Moore.org or follow @MooreFound. The foundation's $185-million EPiQS initiative promotes discovery-driven research in the field of quantum materials (http://www.moore.org/programs/science/emergent-phenomena
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sponsorships (including H-1B visas not requiring consular processing) may be available, as appropriate. Applications must be submitted online at https://www.princeton.edu/acad-positions/position/39502, and
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docking to investigate viral spike/capsid architecture, thermal stability, and immune epitope accessibility. AI/Deep Learning: ML/DL model development (e.g., PyTorch, Lightning) for predicting antigenicity