2,981 phd-studenship-in-computer-vision-and-machine-learning Fellowship research jobs
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computer chips. Your role will be to design processes for transforming resources into sustainable materials by modeling reaction pathways from first principles. The project will collaborate with a broad
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transplantation. We are looking for a highly motivated candidate with a PhD degree interested in using microbiological methods with the aim of developing novel strategies to improve bone marrow transplantation and
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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infrastructure security R&D. Job Requirements: A PhD in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline. Proven research track record demonstrated by publications in top
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the metropolitan area of Marrakech. The School of Computer Science at Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco, is seeking a postdoctoral candidate in the area of federated learning and wireless
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of the Equal Opportunity Act 1984 (WA). About the Role: Curtin University is seeking a full-time Program Manager, Justice Health (official title: Research Fellow) for a two-year opportunity with the option
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requirements: PhD Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field, obtained within the last five year Research Experience in one or more of the following
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must take part in the University’s/ Faculty’s/ Department’s approved PhD program and is expected to complete the project within the set fellowship period. The main purpose of the fellowship is research
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning