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Design. With some 200 staff and students, it offers a friendly and international work environment Learn more about CQT at https://www.cqt.sg/ Roles & Responsibilities Administrate CQT PhD students
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savings options Employee and dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu
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and memory systems. This effort is truly trans-disciplinary, drawing on biodesign/biotechnology, machine learning, and interaction design. This project builds on groundwork already underway in our group
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of digital twins, high-performance computing and AI/machine learning for fusion design and operation; verification, validation and uncertainty quantification for fusion safety and licensing; development
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ties with the Faculty of Computing and Data Sciences, where shared interests in machine learning, large-scale data analytics, and Earth system modeling provide natural ground for joint advising, co
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the application of machine learning), and optimisation of power systems for grid operators and energy producers The scope, depth and quality of the applicant’s scientific work The ability to independently conduct
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analysis, or assistive technology; Experience in applying AI methods such as machine learning, deep learning, computer vision, multimodal data analysis and large language models (LLM) in health-related
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with its grounding in cognitive theory. The position suits candidates with a PhD in Human-Computer Interaction or a related field who enjoy making empirical, methodological, and technical contributions
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explored to analyse the high-dimensional Raman and autofluorescence datasets. Machine-learning approaches will be used to identify spectral features and molecular signatures associated with infection
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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations