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Python or R languages. Experience with differential expression analysis is desired, as is experience with non-model organisms and phylogenetics. Ability to work both as part of a team, and independently
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of delivering interventions for behavioural change. Strong training in experimental and quantitative methods, including experience with SPSS, STATA, R or Python, an understanding of multivariate statistics and
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administrative needs of the Faculty. Based around a core technology stack of Python, PhP, Django, GitLab and SharePoint, often overlaying database technologies such as MySQL and Postgres. The team uses Jira
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expertise in machine learning and data-driven modelling, particularly probabilistic methods, alongside proficiency in scientific programming (e.g. Python, MATLAB). Knowledge of the steelmaking process would
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(essential) extensive experience in programming in Python or Matlab and data analysis (essential) experience with behavioural and neuroimaging (fMRI, M/EEG) data design/collection/analysis (essential) track
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implementations of our models to align more closely with their mathematics. The AIA's modelling toolboxes (there is broadly one per aviation system being modelled) are written in modern typed Python. About 90% of
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programming and software skills (Python essential; C++/Fortran beneficial) Interest in machine-learning-based regression and surrogate modelling Motivation to challenge established CFD-driven design workflows
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degree in Engineering, Physics, Mathematics or a related area 2) Specialist knowledge and skills The role holder will have some experience and skill with programming, e.g. with python, C++, Matlab
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in C++ and/or Python is expected, and experience in model analysis and parameter optimisation is beneficial. Experience in machine learning and neural networks is desirable. The successful applicant