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                Field
 
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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, requiring large computational effort to assess and study system stability. This is becoming even more challenging under increasing complexity requiring detailed dynamical models and with new dynamic phenomena
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of childhood cancer death. You will work on the INSTINCT-MB programme, which brings together teams based at Newcastle University, The Institute of Cancer Research and University College London. The programme
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modelling. The Hub provides cutting-edge laboratory infrastructure alongside SEMS's extensive facilities portfolio. You will work under the supervision of Dr. Salvatore Grasso (https://www.sems.qmul.ac.uk
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computational methods to optimise the quality of doubly curved shell structures manufactured from recycled, short-fibre composites. A particular novelty of the research will be the inclusion stochastic elements
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comprehensive laboratory grinding tests on various rail grades to train and validate the ML model. Utilise numerical modelling to establish acceptable thresholds for surface quality metrics, such as the 'white
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, understand, characterise, and model the effect of surface roughness on wall turbulence when out of equilibrium. Particular emphasis will be made on determining if any form of flow similarity exists across
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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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-free stipend based on the UKVI amount (£20,780 for 2025-26). We expect the stipend to increase each year. This studentship is related to a multi-institutional EPSRC Programme Grant “AMFaces: Advanced