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representation for multi-model fusion”. Qualifications Applicants for the Postdoctoral Fellow post should have a PhD degree in Computer Science, Electrical and Computer Engineering or a related discipline or an
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 3 hours ago
experience in statistics, data science, machine learning, bioinformatics, quantitative biology, or closely related fields. Candidates must possess strong scientific programming skills, demonstrated experience
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and Mathematical Sciences | NTU Singapore We are looking for a Research Fellow to study quantum materials via Machine Learning. The role will focus on develop Machine Learning technique to help DFT
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if the candidate can self-learn the knowledge within a short time (e.g., 1 month). Have a degree in computer science, computer engineering, electrical engineering or equivalent. Possessing a Master’s or PhD degree
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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-scale modelling, spectroscopic analysis, and more. The group has extensive research experience in theoretical calculations, energy science, and machine learning. The research emphasizes the integration
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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for publication. Preferred qualifications: Experience with super-resolution microscopy, coding (Phyton, Matlab), and machine learning is preferred. City of Hope employees pay is based on the following criteria
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in