664 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Nanyang Technological University
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly
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learn to apply these processes judiciously while maintaining a focus on originality and professional integrity. Candidates should have a relevant Master’s and/or PhD in Digital Arts, Computational Media
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Research Fellow to pursuing
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, 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 Key Job Purpose: We are looking for an early-career Analytical Specialist
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. Learn more about our lab at https://www.richardshelab.com/ . Key Responsibilities: Design and lead a novel research project aligned with your scientific interests and the lab’s experimental strengths
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The National Institute of Education invites suitable applications for the position of Research Assistant on a 24-month contract at the Science of Learning in Education Centre . Project Title
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developing innovative solutions for integrating geophysical data and machine learning approaches for geological modelling and site characterization. The position is expected to perform independent and
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teamwork and verbal, written communication skills In-depth knowledge of computer vision and deep learning Demonstrated capability to conduct innovative research We regret that only shortlisted candidates
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programming skills in Python and familiarity with AI/ML frameworks. Good understanding of LLM architectures, transformer models, and machine learning fundamentals. Experience with model serving / inference