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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
development of the FAST Virtual Lab, a digital framework combining experimental data, physics-based models, and data-driven methods to support design, manufacturing, and decision-making across aluminium value
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developed the SPARCED pipeline to convert structured lists of species, parameters, and reaction types into an SBML (Systems Biology Markup Language) model file, and we created a model based on one
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27th April 2026 Languages English English English The Department of Structural Engineering has a vacancy for Two PhD positions in “Micromechanics-based modelling of ductile failure in high-strength
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growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML
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animal models (mouse or, alternatively, zebrafish), preferably as infection models. Experience in working with bacterial cultures (S. aureus). Very good command of English. LanguagesENGLISHLevelExcellent
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understanding, modeling and simulation, iterative and confident progress, justification, and decision support. To this end, the ISOAR team is promoting a research focus on model-based engineering (MBE) and model
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concurrently heated with an additional external stimulus. Photoacoustic sensing (introductory video: https://youtu.be/2f3V0DQNLYg ) is based on a transformation of absorbed optical energy into ultrasound waves
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biomimetic materials, self-healing concrete, extreme wave and flood modelling, IoT and intelligent control systems, renewable energies, robotics, automation, and supply chain management with academics and
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, LDAP, TLS, and HTTPS Knowledge of multi-media presentation and lecture capture systems desired. Knowledge of IP based video conferencing systems desired. Understanding of high-performance computing a
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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets