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ERC-funded postdoctoral fellow in theoretical developmental biology, using tools from applied mathematics, biophysics, and machine learning A talented and creative researcher is sought to take part
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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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bioinformatic workflows. Familiarity with biomedical ontologies and text mining on Electronic Health Records and biomedical literature Knowledge of machine learning / deep learning with an interest in
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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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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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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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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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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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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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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