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., Hidden Markov Models) Experience with statistical software (for example R) Affinity with clinical populations Furthermore your suitability is ideally exemplified by: An ambition for high quality research
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of repeatedly solving wave equations. In the project, we will develop a new mathematical and computational framework that combines PDE-based modelling with ideas from data-driven reduced-order modelling. The aim
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Explore trace element thermodynamics in high-temperature systems to control steel quality in the green transition—a PhD bridging experiments, modelling, and industrial impact. Job description The
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hydrogen environments. By combining advanced experiments with multiscale modelling, CIRHY aims to enable reliable and sustainable steels for future hydrogen infrastructure and industry. Hydrogen
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, these flows remain poorly understood. As a result, even the most basic properties cannot be predicted reliably. For instance, the best available models over- or underestimate the measured pressure drop in a
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Design the simulaton models that will help airlines fly future aircraft types profitably
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
Design the simulaton models that will help airlines fly future aircraft types profitably and sustainably before they take off! Job description Research and industry are increasingly investing in new
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cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible sequence spaces. You will be embedded in one of the three research
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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
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: Developing a system-theoretic framework that models learning as the feedback interconnection between continuous-time circuit dynamics and optimization algorithms. Designing novel energy-based learning