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Field
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statistical methods, and their application to healthcare technologies research. Essential Application Excellent programming skills in Python, Julia or similar, including scientific computing and code curation
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. Possess experience of using Linux operating systems. You will bring: Fluency in programming languages such as Python, R, and C++. Strong analytical and problem-solving skills to identify and resolve issues
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statistical methodologies and experimental design and power analysis. Skilled in required programming languages such as R and Python. A commitment to best practices in data security, governance, and analysis
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spectrometry would be advantageous as would experience using R and/or python code for bioinformatics data analysis of multiplex datasets. Knowledge of MATLAB, Perl, CellPhoneDB, CellChat, NicheNet, Seurat. In
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and TypeScript / JavaScript, although development of established applications and extensions of third-party products may include the use of Python, Rust and other languages. Process automation and one
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commensurate to their experience, have demonstrable experience in developing, implementing and using omics pipelines, be proficient in R and Python programming and in statistical analyses. Candidates should also
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the value that significant and relevant work experience can bring to an individual’s skillset, particularly, if you have experience in at least one aspect of Data Science, Artificial Intelligence, or Python
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languages such as Python, IDL, FORTRAN or Matlab. A deep understanding of space weather processes and their effects on planetary ionospheres is essential, along with evidence of publishing in peer-reviewed
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departments and external partners. About you You will hold a PhD or equivalent in a relevant field and bring substantial experience in developing complex AI or machine learning models, particularly using Python
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field Experience in teaching or training within higher education or research settings Proficiency in bioinformatics tools and methods, including scripting in R and/or Python Familiarity with omics data