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Research theme: Nuclear thermal-hydraulics How many applications: 1 How to apply:uom.link/pgr-apply-2425 This project is co-funded by EDF R&D UK and the University of Manchester. Funding covers
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disorders, ideally idiopathic scoliosis experience with data analysis using software such as Stata or R experience with processing and managing large data sets excellent academic writing skills Personal
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, immunology, high dimensional omics analysis, or mass spectrometry. • Strong data analysis skills using R, Python, or equivalent. • Excellent verbal and written communication skills. • Ability to work both
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in quantitative methods (reflected in courses and/or research experience) Proficiency in R, Python, or similar programming languages (or strong skills in another statistics software) Knowledge about
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programming tools (e.g., Python, R, or MATLAB), ideally with experience working with spatiotemporally explicit datasets in raster or NetCDF formats. Familiarity with optimisation techniques such as mixed
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the simulation results. This work builds on our previous research on crosslinking at the substrate/polymer interface: Suzanne Morsch, Yanwen Liu, Kieran Harris, Flor R. Siperstein, Claudio Di Lullo, Peter Visser
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to: Be trained to work on the collaborative PLAY study. Work on coding and collating the dataset including coding using the R program language for data cleaning and analysis. Organize lab meetings and
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, SAS, R, Stata, and relational database (e.g., SQL) tools Experience with statistical analysis in applications such as SPSS, SAS, R, and Stata using syntax/command files, macros in SPSS, SAS, and/or
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, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
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business datasets (e.g. ORBIS, Foreign Direct investment Data - UNCTAD) and appropriate experience with statistical software (e.g. Stata, R, Python). They will have a good understanding of, and interest in