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strong scientific interests and self-motivation. They will have a degree in physics, mathematics, oceanography, meteorology, or a related science with good computing and numerical skills. Entry
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requirements: You should be enthusiastic and self-motivated with a first-class/high 2:1 degree in a relevant engineering or physical science discipline (e.g., geophysics, earth science, geospatial
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of wider research effort to achieve a clear understanding of this century-old scientific challenge, transforming it from a persistent engineering problem into a well-understood physical phenomenon. Its
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, process stability, and the downstream consolidation and performance of remanufactured composites. This fully-funded PhD project fits within a wider research programme with industrial partners and an
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at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . WE OFFER The position will be filled for a period of 4 years (2 + 2). The starting date is in October 2025 or as
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applicant must have (or be close to obtaining) a relevant PhD in Fluid Mechanics from an Engineering, Mathematics or Physics Department, a strong background in theoretical and computational fluid mechanics
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(AI) & Deep Reinforcement Learning (DRL) for energy optimization ✔ Predictive Maintenance & Failure Analysis using Machine Learning and Physics-Based Modelling ✔ Data Science & Advanced Analytics with
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engineering subject such as Computer Science, Aerospace Engineering, or Physics. If English isn’t your first language, you’ll need an overall IELTS score of 6.5 with at least 6.0 in each section Eligibility
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Job description: A Research Assistant / Associate position, funded by a Wellcome Trust Collaborative Award in Science, is available in the research group of Prof. Dylan Childs. The grant is led by
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We are seeking a research assistant with a background in computing to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device