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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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A University of Manchester The University of Manchester enjoys a global reputation for its research and its innovative approach to learning and is one of the largest single-site universities 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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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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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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-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
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The job details are shown below. For further information click on the link for the job description. Are you an enthusiastic, competent and proactive team player, who is highly motivated to learn