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technical ideas, and engaging with GenAI industry and open-source communities. Job Responsibilities: Conduct the research in AI/ML domains, especially in probabilistic ML and scalable sequence network
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community, and develop their teaching and research-leadership experience. Engagement with Dstl, project partners and relevant industry and government stakeholders will help the Fellows build external networks
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on their interests and experience, the Fellows will also have opportunities to co-supervise undergraduate and postgraduate research projects, contribute to the wider research community, and develop their teaching and
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policy, research and more? Do you want to develop skills within and outside of your field of study and interest and earn a competitive edge? Do you want to network with your peers, travel, and participate
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technology, ethics and machine learning, in new and unique ways. The focus of Integreat is to develop ground-breaking methods and theories, and therefore solving fundamental problems in science, technology
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will
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: _vid_t Leverandør: .jobbnorge.no Databehandlingsansvarlig: Formål: Saves the selected language for the website. Utløpsdato: én måned Navn: jobbnorge.language Leverandør: .jobbnorge.no
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grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. The College of Computing and Data Science is seeking to hire a
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 19 hours ago
regression, principal component analysis, and artificial neural networks — to determine optimal modeling frameworks for high-dimensional in vitro data generated by platforms such as organ-on-a-chip systems and
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with GenAI industry and open-source communities. Key Responsibilities: Conduct the research in AI/ML domains, especially in probabilistic ML and scalable sequence network architectures Produce academic