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Field
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in
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sits at the cutting edge of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behaviour at the grain scale. Your work will form a key part of a
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Establishment). Recent work by the group (leading to REF 4* rated outputs and several Keynotes) has contributed to bridging the gap between Computational Solid and Fluid Dynamics, with a unified computational
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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the current thermo-mechanical process use to strengthen the current generation of crush alloys. Programme will use different thermomechanical processing paths including heat treatment and more complex paths
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Leibniz Association. The following position is available at the Institute subject to approval by the funding organization from October 1, 2025, for a fixed term of three years, in the program area "Next
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of the Leibniz Association. The following position is available at the Institute subject to approval by the funding organization from October 1, 2025, for a fixed term of three years, in the program
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numerical calculation skills and mathematical modelling skills Strong skills in solid state physics and quantum mechanics Experience in theoretical modelling and experimental investigation of optical devices
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this position, you are expected to have: A solid foundation in concrete technology, mechanics of materials, and structural engineering A strong interest in experimental research Prior exposure to or interest in
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of research results in scientific journals and conferences. Qualifications: Good understanding of solid mechanics and preferably modeling of damage and/or fracture. Experience with experimental work and data