359 computer-programmer-"University-of-St"-"University-of-St" Fellowship positions in Singapore
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of research. Plan and manage own research activity in collaboration with others. Communicate material of both a non-technical and specialist nature to a broad audience using a range of media. Carry out Risk
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Research Design and Execution: Design and implement qualitative research studies that address pressing challenges in ageing and population health. Conduct applied research focused on informing programme
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) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena
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We are seeking for a full-time Postdoctoral Fellow to work on projects in the area of household finance, investment, and financial literacy. The project is part of a large 5-year research program
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, Computer Science, or related field, with strong background in machine learning Publications in top-tier journals and/or conference presentations as first author Proficiency in AI programming languages
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people within (staff and PIs/co-PIs within the programme) and external to the university (industry partners and funding agencies). Self-directed learner who believes in continuous learning and development
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approach to achieve a goal, once a problem statement and/or a research scope is defined by the PI and the staff. The staff will need to execute the plan experimentally and interpret the data critically
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cilia and nuclear signaling in the brain. Position Summary: We seek a candidate with strong expertise in microscopy and computational image analysis to: Support and enhance ongoing imaging projects in
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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the industry to lead and/or conduct innovative research on, but not limited to evolutionary computing, job scheduling, transfer optimization, transfer learning, reinforcement learning, large-scale