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Field
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. Willingness to support project reporting and milestone reviews. Hard skills Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Experience in autonomous navigation
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About the Role The Centre for Epidemic Response & Modelling (CERM) at NUS Saw Swee Hock School of Public Health seeks a Research Fellow with deep expertise in Bayesian statistical modelling and
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine
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/moisture retention in soils, reducing soil compaction, attracting pollinators, etc., but the overall impact of cover crops on rootzone soil moisture availability and deep percolation is not well understood
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, procurement, or grant administration will be an added advantage. Experience in areas such as Machine Learning, Deep Learning, Computer Vision, Large Language Models, Data Analytics, or Intelligent Systems will
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requirements: Candidates must hold, at the time of application, a Bachelor’s degree in Informatics Engineering or related fields. Candidates must also have knowledge in: i. Deep Learning and LLMs: practical
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algorithmic improvements from recent literature in AI integrative multi-omics analyses. Develop new deep learning algorithms, from theory to experimental implementations, or adapt/integrate existing ones
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intelligence, machine learning, deep learning, environmental modelling, climate-health research, early warning systems, or related fields. Have strong programming and computational skills in Python, R, MATLAB
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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experience in the following areas: deep learning and/or computer vision continual learning vision language models Experience in developing comprehensive benchmarks. Robust coding and software engineering