28 linked-data-"https:"-"https:"-"https:"-"Itä-Suomen-yliopisto" PhD positions at The University of Manchester
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may be removed before the deadline. Modern low-carbon energy systems such as photovoltaic (PV) arrays and battery energy storage systems (BESS) generate extensive measurement data (electrical, thermal
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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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learning approach to translating multi-modal inspection data into remaining useful life predictions; and (3) create a dynamic techno-economic model linking real-time condition assessments to optimal
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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before the deadline. Computational haemodynamic modelling provides a powerful framework for linking blood flow dynamics with cardiovascular disease, using in silico approaches to systematically study flow
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of die compaction — from individual crystal behaviour to the formation of solid pharmaceutical tablets. Spanning many orders of magnitude in scale, the work will link particle-scale interactions, such as
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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that link between processes, microstructures and function. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or
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join the Catalysis and Porous Materials group at Manchester, a well-established research community with strong infrastructure, a track record of industry engagement, and active links to national
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stable isotope probing linked to multi-omics analyses. These system-level measurements will be integrated with analysis of the metabolic potential of our experimental communities using strain-resolved