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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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manufacturing. As steel is highly recyclable its usage helps in creating a more sustainable world. For simulations of industrial processes concerning such complex materials one must typically rely on continuum
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project aims to address the knowledge gap by investigating: - How ML methods can be adapted for complex, real-world conditions, including noise and interference, - How such methods can be
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Support Grant of up to £5,000 Access to Disabled Student Allowance, paid sick leave and paid parental leave Supervisor: University of Warwick: Dr Arnab Palit, Prof Andy Metcalfe Eligibility: Satisfy UKRI's
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“Light- versus electron-induced spin-state switching of complexes on insulating layers” within the Priority Programme SPP 2491 “Interactive Spin-State Switching” This DFG-funded project aims
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embedded in the research programme of FEB’s Research Institute. The project will be supervised by Prof. Robert Lensink (Faculty of Economics and Business), email: b.w.lensink@rug.nl , Prof. Han Olff (Faculty
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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Company description The Lab for NanoBiology (Prof. Dedecker) is an interdisciplinary research group focused on the study and visualization of molecular processes occurring in biosystems. We combine
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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 German Research Foundation (DFG), at the University of Tübingen. The project is led by Principal Investigators Prof. Dr. Michael Franke, Dr. Marlen Fröhlich (both Tübingen) and Prof. Dr. Manuel Bohn