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Field
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The Centre for Doctoral Training in Nanoscience and Nanotechnology (NanoDTC) at the University of Cambridge invites applications for its 3.5-year interdisciplinary PhD programme. The programme
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this fundamental challenge, the PhD candidate will be part of a wider team to establish methodological framework, combing utilisation of controlled tree growth test, thermodynamic modelling and advanced optical
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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quantitative data, including spreadsheets, emissions databases, or modelling tool possess strong analytical and communication skills, with the ability to engage and collaborate effectively with both academic
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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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