367 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in United Kingdom
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for life-long learning and careers in engineering for engineering students, and in technology for technology students; with emphasis on leadership skills and creative work so they can lead cultural, social
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Supervisor: Professor Fernanda Duarte Start date: 1st October 2026 Applications are invited for a fully-funded DPhil studentship in Machine Learning Interatomic Potentials for Metal-Ligand
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Automatization and Digital Enhancement of Characterisation Techniques: Joining the Dots between AI, Machine Learning and Materials Advances School of Chemical, Materials and Biological Engineering
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order to detect microseismic events, characterise their sources, and image geophysical perturbations caused by industrial activities. The development of machine learning methods to efficiently process
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leading School. In this role, you will: Teach at undergraduate and master’s level, including lectures, seminars, tutorials and computer labs. Act as a personal tutor, providing academic and pastoral support
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skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning approaches. Experience in
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support the development and learning of the scientists & engineers of tomorrow? If you are, we would love to hear from you. You will provide essential specialist technical support to researchers and
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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problem‑based learning (PBL), applied learning including simulation, and a strong emphasis on early patient contact. You will to be responsible for designing and developing learning materials and will make
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human