307 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" Fellowship positions in Singapore
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for a talented and motivated postdoctoral fellow to join the Genome Re-InnovaTion Lab (https://grit-lab.org), part of the Synthetic Biology Translational Research Programme at the National University
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PhD qualification degree in Electronic Engineering or Computer Science Familiarity with pinching antennas and machine learning Good written and oral communication skills Proficiency in python
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discoveries into meaningful health outcomes for patients, Singapore, and the global community. For further information, please visit: https://www.ntu.edu.sg/medicine/CMM . We are seeking a motivated Research
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role in designing composites materials using inorganic solid electrolytes using computational modelling and machine learning. Qualifications • Ph.D. in Materials Science, Chemistry, Physics, or a
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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, modulation instability, and supercontinuum generation. Integrate experimental data with AI models, using machine learning to uncover hidden physics, accelerate simulations, and discover new operational regimes
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fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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/ machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian You will be part of a dynamic research team working on topics relevant