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; Chemistry; Computer Science; Ecology; Engineering; Environmental Sciences; Mathematics/Mathematical Sciences; Medicine; Natural Sciences; Physics; Psychology; Veterinary Sciences. This Fellowship is intended
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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regimes of deep networks. Basic Qualifications: A PhD is required. We seek candidates with strong analytical and numerical skills, and backgrounds in physics, theoretical neuroscience, applied mathematics
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Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research
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to be agreed with the successful candidate. We invite applications for a Postdoctoral Research Associate to join the OCEAN project (On intelligenCE And Networks: Mathematical and Algorithmic Foundations
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-motivated researcher 2. Good paper reading and writing skills 3. Excellent publication record, including top-tier journals and conference papers in the related fields. 4. Strong in Mathematics with good
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The Singapore Centre for Environmental Life Sciences Engineering (SCELSE) is a unique interdisciplinary Research Centre of Excellence (RCE), funded by National Research Foundation, Ministry of Education, Nanyang Technological University (NTU) and National University of Singapore (NUS). Hosted by...
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Required knowledge: biology, chemistry, mathematics, statistics, documentation, records management, data utilization, complex problem solving, critical thinking, resource management, and writing skills
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women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEM). Further details Job Description
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science, computational biology, engineering, statistics, mathematics.). Experience and interest in prediction modelling Proficient in statistical software and programming languages and familiarity with relevant libraries