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and validation of compositions identified by the modelling. Applicants should have, or expect to achieve, at least a 2.1 honours degree or Master’s (or international equivalent) in Chemistry, Materials
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Start Date: 01/10/2026 Application deadline: 26/03/2026 Applications are invited for a fully-funded Ph.D. studentship in the Department of Aeronautics in structural power composites. This PhD is
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manufacturing processes, especially for the transportation sector and medical technology. To this end, materials are tailored and manufacturing processes are designed to conserve resources – from the modeling
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Tomography (CT) – two key NDT techniques used to inspect metallic and composite materials. These methods generate large 3D datasets that can be difficult for human experts to analyse manually, increasing
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gases. As energetic particles propagate through air, they interact with the air molecules and produce radicals, ions and excited species which can alter the chemical composition of the atmosphere
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mangament in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. Scientific context Many engineering and
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(DLS) and laser diffraction granulometry, alongside the surface properties of the particles — notably their electrokinetic charge, hydrophobic/hydrophilic balance, and surface chemical composition (FTIR
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, their composites, and analyse, characterise, or model the reaction pathways and the properties of involved phases. The role holders must have very good knowledge and expertise in material physics and/or chemistry or
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distribution of debris covered glaciers; the chronology of their deposition, deformation and degradation; and their dynamics and composition. Each of these attributes plays a crucial role in unravelling
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date