856 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions in Singapore
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machine learning by designing and developing innovative models and algorithms. Key Responsibilities: Designing and conducting comprehensive research in artificial intelligence and machine learning, with a
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-scale modelling, spectroscopic analysis, and more. The group has extensive research experience in theoretical calculations, energy science, and machine learning. The research emphasizes the integration
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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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, reinforcement learning, AI agents, and machine learning. Responsibilities: Conduct AI research Present research outputs Organize project relevant events Manage project progress Write research papers/reports based
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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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algorithms and system frameworks that optimize cost, performance, and scalability. The role focuses on leveraging machine learning and reinforcement learning to enhance storage and service efficiency under
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activities under the supervision of the Principal Investigator. Implement analysis pipelines and machine learning algorithms to analyse high-throughput genomic data, biobank data, and clinical data. Write high
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computer vision and machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to
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). Proficiency and independence in developing Python‑based machine learning / artificial intelligence models (e.g., LSTM or time-series models) to predict the long-term properties of bioactive materials (mandatory
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technology (PAT) adds cost, time, and introduction of human error. This project aims to develop a novel, label-free microbial detection method, coupled with machine learning tailored for cleaning validation