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Field
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electron microscopy image simulations Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images Application of the new approach
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track record in the fields of immunology or infectious diseases, compelling research plans that complement ongoing work at CITIID, and a strong commitment to contributing to a positive, collaborative
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relevant degree in health science (essential criteria). We are looking for candidates who have demonstrated a track record in research, preferably around issues relating to cancer diagnosis, for example
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experience: A strong track record of previous research in multilingual NLP Track record of authoring high quality academic publications Excellent programming skills Excellent oral and written communication
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knowledge of their validity through practical measurements in real conditions. The academic supervision team have a track record of more than 20 years each on multistatic radar research and some 500
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-material capability with a suitable closure model; (2) improved strategy for interface tracking/capturing; (3) very high-speed scenarios with use of nonlinear Riemann-solvers. If time allows exploratory 3D
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to support identification of such deepfakes. Using a mixed-method methodology and eye-tracking technology, we will investigate whether people can reliably identify deepfakes (face images and video clips), what
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easier tracking of applications. Funding Fully and directly funded for this project only for 3.5 years (total £137k): Standard UKRI stipend for 42 months (currently £20,780 per year, tax free) Tuition fees
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provides full UK domestic fees and stipend only, and will track UKRI studentship rates , in addition to fieldwork and wider academic development support. The successful candidate will be enrolled and based
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track record, with scientific curiosity and a commitment to rigorous research. Strong written and verbal communication skills in English. Experience of working in a team environment. Experience of working