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Field
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to improving materials design and performance predictions for crash-relevant loading conditions, enhancing crashworthiness and structural integrity in lightweight automotive components. Your work will bridge
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Optimization (AI/ML) Developing AI/ML models to predict drillability issues based on mechanical rock properties Real-time parameter optimization (WOB, RPM, flow rate, etc.) using machine learning techniques
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
microstructure evolution during extrusion is critical for controlling final mechanical properties and surface appearance of extruded profiles, yet quantitative predictions remain challenging due to the complexity
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downhole and mitigating them in real time 3. Data‑Driven Drilling Optimization (AI/ML) Developing AI/ML models to predict drillability issues based on mechanical rock properties Real-time parameter
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Job Description Join a young research group dedicated to transforming materials discovery from empirical exploration to predictive design. Our research program builds on a synergy of high-throughput
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strategic raw material (CRM) deposits within Paleoproterozoic cover sequences that overlie an Archaean basement. Existing structural models for the cover rocks predict that crustal-scale fault and shear zone
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physiology and breeding. The main goal for the PhD project is to help elucidate the environmental and genetic control of maturation in faba bean under Norwegian growing conditions. Faba bean has gained
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, international group of 13 researchers from 8 countries, with expertise across energy systems and markets, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the
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, large-scale foundation models will be developed and trained on the Aalborg Supercomputer (TAAURUS), facilitating advanced ECG representation learning and prediction of acute coronary events in
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be applied to core technologies of the department, e.g., batteries and catalysts. The project includes collaboration with experimentalists at DTU, who will verify the computational predictions as