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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 8 hours ago
experience in statistics, data science, machine learning, bioinformatics, quantitative biology, or closely related fields. Candidates must possess strong scientific programming skills, demonstrated experience
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the area of probability of statistical machine learning. They will possess sufficient specialist knowledge in network analysis and uncertainty quantification in machine learning and have the ability
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infertility, pregnancy, lactation and developmental programming, urogynecology, artificial intelligence and machine learning are particularly encouraged to apply. The Department of Obstetrics and Gynecology
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of collaborators spanning CERM, NUS, Imperial College London, Ashoka University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global
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and Mathematical Sciences | NTU Singapore We are looking for a Research Fellow to study quantum materials via Machine Learning. The role will focus on develop Machine Learning technique to help DFT
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or machine learning applied to brain signals would be an advantage. We are seeking a highly motivated, rigorous and inquisitive researcher, ready to commit to a project at the interface between basic and
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if the candidate can self-learn the knowledge within a short time (e.g., 1 month). Have a degree in computer science, computer engineering, electrical engineering or equivalent. Possessing a Master’s or PhD degree
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Fund project TARGETWISE. The candidate will be responsible for conducting machine learning omics data analysis within the computational team. The details on responsibilities, obligations and rights
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and summarize results, publish in scientific journals, and contribute to the supervision of students. Your profile PhD degree in bioinformatics, machine learning, computer science, biochemistry
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) Experience in genomics, single-cell data, or machine learning (preferred) Why this is exceptional Build next-generation AI models of the human genome Work with one of the richest longitudinal PD datasets