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establishing a strong academic track record. You may have worked in MRI research previously or have strong computational / AI / machine learning skills used in other areas of research. Essential criteria PhD
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to contribute to teaching and learning for diagnostic radiography. Candidates should have current professional registration (on the appropriate part of the register) with the Health and Care Professions Council
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and learning adaptation to uncertainty. You will use fMRI and neurostimulatory techniques (ultrasound neurostimulation and/or transcranial magnetic stimulation) to test the causal role of targeted
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large-scale speech and wearable data from participants in the GLAD Study cohort ( https://gladstudy.org.uk/ ). Using large language models (LLMs) and acoustic analytics, they will uncover patterns in
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interest, narrowing the scope to natural or cultural sites, and integrating diverse remote sensing datasets. The supervisory team offers interdisciplinary expertise in geospatial analysis, machine learning
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and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers. The optimal design of superconducting qubits is a highly
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methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and academia. The candidate
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. Interested candidates may want to take a look at our recent work on machine learning molecular dynamics: https://www.nature.com/articles/s41467-024-52491-3 Project 2: Non-adiabatic Molecular Dynamics
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on quantitative phenotyping via generative modelling of quantitative MRI data. This exciting PhD position combines advanced machine learning with medical imaging physics to develop next-generation tools
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep