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proficiency in machine learning, statistical modeling, and data analysis using Python, R, or similar platforms. Experience in grant proposal writing, scholarly manuscript preparation, and psychological
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international conferences. Required Qualifications* PhD in computational biology, bioinformatics, data science, or a related quantitative field. Proficiency in Python and/or R; experience with high-performance
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Qualifications* PhD in computational biology, bioinformatics, data science, or a related quantitative field. Proficiency in Python and/or R; experience with high-performance computing environments. Experience with
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multiple local and international collaborations. She is shortlisting a Research Fellow to provide research support in the analyses of methylome sequencing (whole and targeted) data, with the purpose
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Posting Start Date: 23/03/2026 Job Description The Saw Swee Hock School of Public Health (SSHSPH) at the National University of Singapore (NUS) is seeking multiple determined full-time Research Fellow
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multiple LLM-powered agents operate concurrently to explore user-generated worlds and trigger diverse contextual scenarios. Develop a unified multimodal detection pipeline that integrates video, audio, and
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implementing monitoring, visualization and control system to coordinate multiple energy sources including battery energy storage systems (BESS), solar PV, and diesel generators, and dynamically interacting with
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, or unsupervised learning methods. Proficiency in Python and familiarity with scientific computing libraries such as PyTorch, TensorFlow, Pandas, NumPy, and related ML frameworks. Experience with large datasets
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The research will advance the use of multiple systems
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environmental and ecological data; knowledge of, or interest in, statistical data analysis using programming tools (R, Python, MATLAB, or equivalent); and the ability to work collaboratively in interdisciplinary