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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
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, challenging project. Learning on the job isn't just a benefit – it's a must. Education, Qualifications and Experience Essential Criteria Applicants should hold a PhD in a relevant area of Engineering
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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Full-time: 35 hours per week Fixed-term: 31st March 2026 The School of Informatics at the University of Edinburgh invites applications for 2 Post-doctoral Researcher positions in Quantum Machine
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning
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: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing Good communication skills and an appropriate publication record are essential. Solid knowledge of Python and C++ is
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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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opportunity to teach. Applicants should hold, or be close to completing, a PhD in plasma physics or high-power laser-plasma interactions. They should have extensive experience of working with particle-in-cell
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-electron Schrödinger equation for fermions and bosons with high accuracy and on the application of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning