94 parallel-programming Fellowship positions at Nanyang Technological University in Singapore
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required to substantially advance a research programme focused on next-generation optical imaging and sensing technologies, aimed at pushing the boundaries of performance and enabling new capabilities
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for future career success, but also have a good quality of life and wellbeing, thus supporting a flourishing society. The CLIC program is seeking One full-time Research Fellow (with potential opportunities
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Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
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the scope of the CLIC programme. Job Requirements: PhD degree in experimental psychology, biopsychology, cognitive psychology, neuropsychology, neurology, psychiatry, cognitive neuroscience, biomedical
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optimization problems Develop mathematical modeling framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data
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purification, and various biochemical techniques. Cryo-EM sample preparation, grid optimization, high resolution data collection, data processing using various programs, interpretation, 3D reconstruction, model
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/fireworks/aflow). Proficiency in at least one programming language for data science and for numerical computing (e.g., Python/Julia/Fortran). We regret to inform that only shortlisted candidates will be
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for Singapore’s adult learners. Adapted from the TransformUS!™ Higher Ed program (Deakin University), the intervention will be implemented in local Institutes of Higher Learning and tested for its impact on
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, or a related field. Proven experience in large/small language model development, fine-tuning, or LLM-based applications. Strong programming skills in Python, and familiarity with key libraries such as
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model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy