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Field
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, Python, Mplus, or similar) - Familiarity with big data, Linux systems, and remote computing clusters - Experience with genome-wide statistical genetics approaches (e.g., GWAS, genetic correlations, PGS
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programming or scripting skills (e.g., Python, MATLAB, Bash) are a plus Prior experience in amyloid biology, protein aggregation, or disease-related structural research are an advantage. Ability
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statistical and machine learning methods, particularly for high-dimensional or longitudinal data. Demonstrable experience in research software development using R or Python and a track record of contributing
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programming languages for data analysis (e.g., R, Python). Experience with mass spectrometry-based metabolomics approaches, including LC-MS/MS. Experience operating, maintaining, and troubleshooting LC-MS/MS
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across large cohorts (e.g., >300 samples) demonstrated experience in data analysis, visualisation, and machine learning, using programming languages such as R or Python excellent interpersonal, verbal, and
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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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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
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communication skills in written and spoken English. It is a merit if the candidate has skills in programming languages (e.g. python or R) and good knowledge about agricultural cropping systems and ecology. In
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human subject research in clinical populations with impaired neuromuscular control, especially those with acute or chronic concussion Programming experience (MATLAB, Python, R) Experience using
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For Someone Who • Has a PhD in computational biology, genomics, bioinformatics, microbiology, or a related field. • Is fluent in Python/R and Unix-based workflows. • Has experience with microbial genomics