17 parallel-computing-numerical-methods Fellowship positions at The University of Southampton
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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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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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turbulence data, together with fine-scale profiles from standard Argo floats, to quantify rates of vertical and horizontal ocean mixing, and you will apply inverse methods to investigate the role
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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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Experience of using qualitative and quantitative research methods Proven ability to organise a range of high quality research activities to deadline and quality standards Experience of managing and undertaking
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to gender—and demonstrated expertise in advanced statistical methods (e.g., multilevel modelling). The successful candidate will work closely with the Principal Investigator, Dr. Verena Klein, a PhD student
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a PhD in engineering or a related area. In particular experience in experimental methods for aerodynamics is essential. Good prior experience in wind tunnel testing is essential with experience in
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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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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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The University of Southampton are seeking to appoint a fixed term research fellow to facilitate and deliver a programme of research on a Faraday Institution funded project: SL2FBat - Sustainable