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collections interact with power, knowledge, and ongoing processes of heritagization. The candidate is encouraged to develop new methodologies that center community-led, ethical, and innovative approaches
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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) their undergraduate degree in physics, engineering or chemistry (preferably with first class honours or equivalent) and we expect the PhD candidate to develop the expertise required to lead an experimental research
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developments in the field of chironomid palaeoecology include the development of high-resolution analyses to reconstruct decadal-scale ecosystem dynamics of lake ecosystems, the interpretation of influx data
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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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of nanoparticles for healthcare and industrial applications. As a PhD candidate, you will: Develop and refine SAXS and FCCS methods to quantify size, concentration, density and internal structure of diverse
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! RESPONSIBILITIES: You will elucidate the molecular mechanisms driving the development of distinct malignant lymphoma subtypes and contribute to the identification of predictive biomarkers and novel therapeutic
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Job Code: 9521 - Research Assistant and 9529 - PhD Candidate Research Assistant Employee Class: Grad/Prof Student Position This position will provide support for lesson plan development relating
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respond over time (e.g. changing shape), controlled by the arrangement of differential materials within them. The goal of this project will be to develop responsive 4D-printed biomaterial devices for drug