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/scholarships/scholarship-policy-and-procedures The Opportunity This PhD scholarship forms part of a collaborative research program on digital transformation, data practices, and everyday life in Indonesia
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from outside the University of Stuttgart Can attend courses and seminars specially designed for simulation technology Are able to exchange technical and methodical information Are integrated
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three pages), contact information of at least two references, copy of the doctoral degree certificate, and other certificates/grades that you wish to be considered.
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scheme. More about being a Lund University employee - on Lund University's webpage Further information The position is a fixed term employment during 9 months at 100 per cent of full time. The starting
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to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric archetypes (e.g. curved vessels, bifurcations, side branches
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17 Jan 2026 Job Information Organisation/Company University of South Bohemia Research Field Environmental science Environmental science » Earth science Environmental science » Ecology Mathematics
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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simulation experience; collaboration with leading researchers; and training in advanced characterisation, data analysis, and data interpretation. Number Of Awards 1 Start Date 1st October 2026 Award Duration 4
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, and assess their association with health and production performance. For more information and for an initial discussion please contact Dr Lisa Williams Lisa.Williams2@hartpury.ac.uk The University
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will