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degree (2.1 +/MSc) in mechanical / civil / environmental engineering, physics or a related quantitative field; strong computing skills (Python/MATLAB/GIS); interest in transport or urban futures. Position
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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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to the design of novel peptides and peptide aggregates able to induce spontaneous fusion. The peptides have been selected based on their biocompatibility and our exceptional experience with membrane-active
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of the (computational) mechanics of solids and the finite element method and/or spectral solvers Practical experience in at least one programming language (preferably Python) and experience with the use of Unix/Linux
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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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technicians. Applicants should have experience in experimental fluid mechanics and aerodynamics. Prior experience in aeroacoustics is desirable. Applications are welcome from candidates currently pursuing
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Merits: Experience with Matlab Prior coursework or project experience in railway mechanics Background in signal processing Knowledge of machine learning techniques Main responsibilities Your primary
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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to design and perform experiments to identify pathological mechanisms underlying craniofacial pain (e.g. temporomandibular disorders pain, migraine, and eye pain) and arthritis pain (e.g. psoriatic arthritis
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skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning