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class Bachelor’s degree, or a Master’s degree with merit or distinction, in Mathematics, Statistics, Computer Science, or a related quantitative discipline. International qualifications of an equivalent
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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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will be an advantage if candidates have an interest in optimisation, control and computing systems, model checking, mathematical logic and good programming skills, ideally in MATLAB, Python and/or C/C
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence will inform future race strategy and live race tactics. Multiple factors influence the strategy...
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, drugs, antibodies, human lymph system, seconds, days, years). This project is in collaboration with GSK and we will work closely with experts from GSK. The Departments of Mathematics is part of
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Application deadline: All year round Research theme: Control systems and theory, Applied Mathematics How to apply:uom.link/pgr-apply-2425 Number of positions: 1 Open to: UK This 3.5 year project is
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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candidates with: • Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) • Willingness to adapt and work across different
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and