38 parallel-computing-numerical-methods-"https:" Fellowship positions at University of Birmingham
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) and a Grade 8 Research Fellow (106267), however there is only 1 post available. Background We are seeking to appoint an autonomous and experienced qualitative/multi methods grade 8 research fellow to a
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Consortium). The post is based in the School of Health Sciences within the Applied Health Sciences Department, and the post holder will assist with delivering the programme of work associated with the PHRESH
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particular using diffusion tensor methods) of human participants (Fellow 1 only) Experience conducting neuroimaging analyses on high performance computers (HPC), such as BlueBEAR or similar Cooperative nature
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in an exciting period of growth: major investment from the University will deliver a state-of-the-art research building (opening in 2023/24), and new multi-million pound high-performance computing
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-performance computing facilities, which will ensure UK-leading compute capability. These investments build on a major recent expansion of our academic staff and investment in our teaching and learning provision
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pound high-performance computing facilities, which will ensure UK-leading compute capability. These investments build on a major recent expansion of our academic staff and investment in our teaching and
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experimental datasets using advanced statistical and computational approaches, and contribute to the establishment of standardised baseline ranges Coordinate and manage laboratory activities within the iCENC
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appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research including drafting of risk assessments and COSHH forms
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and gravitational-wave astronomy (for a full list of group members, see: https://www.sr.bham.ac.uk/whoswho.php ). We are especially looking for candidates with interest and experience in the theoretical
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or Stata and a strong grasp of statistical methods, data analysis and regression modelling. You will have experience in using large complex datasets, data linkage, data cleaning, and good research writing