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Field
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Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers
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2025. Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with
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dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary
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be awarded a master’s degree in any of the following disciplines: Chemical Engineering, Physics, Mechanical Engineering, Energy Engineering, Environmental Engineering, Computer Science, or other
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(CAD) since 2016 and CT- Fractional Flow Reserve (CT-FFR) as a second line test since 2017. In 2018 a national health technology programme funded CT-FFR utilisation with the aim of improving patient
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Solutions site, so applicants should be prepared to travel independently. Entry requirements Applicants should have a first or second class UK honours degree or equivalent in Chemistry or Chemical Engineering
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£20,780 per year, and includes a 3-month fusion engineering CDT training programme as part of the 2025 Cohort. This project is co-supervised by Dr Chris Hardie from UKAEA. The UoB Materials for eXtremes
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£20,780 per year, and includes a 3-month fusion engineering CDT training programme as part of the 2026 Cohort. This project is co-supervised by Dr Chris Hardie from UKAEA. The UoB Materials for eXtremes
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co-supervised by both academic and industry experts, gaining valuable skills at the interface of hydrogeology, environmental engineering, and computational modeling. Research Objectives (1
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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms