129 parallel-computing-numerical-methods-"Prof" positions at UNIVERSITY OF SOUTHAMPTON
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authoritative practical knowledge and experience in acoustics and/or vibration, supported by detailed understanding. Basic understanding and knowledge of numerical modelling using the Finite Element Method
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knowledge of numerical modelling using the Finite Element Method/Boundary Element Method. Excellent communication and teamwork skills. Informal enquiries can be made to Dr Felix Langfeldt: F.Langfeldt
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and the development of innovative design concepts, maximizing material properties and sustainable manufacturing methods. Qualifications: Strong background in aircraft design. Proficiency in Python
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United Kingdom Application Deadline 30 Sep 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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(animal models) studies in preparation of potential first-human studies. For this purpose, there will be a tight interaction between the Cancer B-cell group and Prof. Mark Cragg at the Antibody Vaccine
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We are seeking a highly motivated post-doctoral researcher of high calibre to join our research program in the area of integrated silicon photonics. The position is funded by the EPSRC (Engineering
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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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range of experimental methods including experimental cognitive psychology, psychopharmacology, psychophysiology and neuroscience methods to better understand the aetiology and treatment of mood and
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which include restaurants, retail outlets and entertainment. Informal enquiries and visits to the centre can be arranged by contacting Prof Roberta Buhagiar, Clinical Coordinator rb22@soton.ac.uk
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental