292 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" Fellowship positions in Singapore
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accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for machine learning and AI acceleration. Perform performance, power, and area
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, Bioinformatics, Computational Biology, or other AI-related disciplines. Strong foundation in AI, statistical modeling, machine learning, or high-dimensional data analysis. Proficiency in programming languages
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@ NUS (https://engbio.syncti.org ) specializes in Synthetic Biology in which we engineer microbes with useful capabilities for medical and industrial applications and we are part of SynCTI at NUS (https
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, including organizing research activities, managing projects, and contributing to grant proposals. Job Requirements: PhD degree in communication, psychology, sociology, Human-Computer Interaction (HCI
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third largest university by intake in Singapore. SIT’s mission is to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society
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position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity
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independently and as part of a team Experience with machine learning and AI applications in engineering is advantageous We regret to inform that only shortlisted candidates will be notified. Hiring Institution
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multicultural hub and a leading Asian center for quantum technologies. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/IRL3654-CORHUN-036/Default.aspx Requirements Research FieldPhysicsEducation LevelPhD
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Requirements: A Ph.D. degree in a related discipline (transportation engineering, computer engineering/science, or related disciplines) by December 2025. Expertise in AI, deep learning, and programming (e.g
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low