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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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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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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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The successful candidate should have: Master’s degree in Industrial Engineering and Management, Mechanical Engineering, Energy Systems, Control Engineering, Data Science, or a related field, with solid foundations
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mechanics and solid mechanics Willing to travel and attend the meetings with industrial partners on site; Should have or be willing to work within a multidisciplinary environment. How to apply Interested
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Job Description The Institute of Mechanical and Electrical Engineering at SDU invites applications for a PhD position in Neuromorphic Brain-Computer Interface Design. Are you a multidisciplinary
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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
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large-scale neural models of the early visual system. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience and strong deep learning