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of robust, wearable neurotechnologies. This role offers an excellent opportunity for a researcher with expertise in neural signal processing and AI/machine learning for the analysis, classification, and
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wide range of research, including quantum information and simulation, artificial Intelligence and machine Learning, and physics within and beyond the Standard Model at current and future colliders. The
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particular focus on applications relevant to the Arab world. The successful applicant will join a multidisciplinary research team working at the intersection of machine learning, algorithmic fairness, human
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Competition Funded Students Worldwide Prof A Tartakovskii, Dr Imad Faruque Application Deadline: 15 January 2026 Details A fully funded PhD opportunity to participate in the world-leading research undertaken
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intervention models will lead to suboptimal decisions, potentially jeopardising the safety of a structure and increasing the costs. Traditional approaches to machine learning establish correlations between some
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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About the role The neurotechnology research team at the University of Bath, led by Professor Damien Coyle, is developing new AI and immersive technologies to advance brain–computer interfaces (BCIs
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power consumption trends or including the energy penalty of machine learning solutions themselves. And the energy efficiency at the transceiver hardware will be put in a broader perspective of
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electromagnetic (EM) theory/modeling, at the Electrical and Computer Engineering Department, and in collaboration with the Applied Mathematics Department, Santa Clara University. This is an in-person position
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London weighting (Starting salary to be confirmed on offer of appointment), plus £2000 per annum training budget Key words: machine learning, deep learning, AI, artificial intelligence, data analytics