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of scientific programming, e.g. with C++, Python, ROOT or similar. Informal enquiries to Professor Dave Charlton, email: dave.charlton@cern.ch View our staff values and behaviours here Use of AI in applications
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or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven ability to work independently. Demonstrated ability
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programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven ability to work independently
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computing (e.g. Python, MATLAB, or similar) for data processing and data handling Experience with data processing, analysis and interpretation Excellent interdisciplinary communication – Strong collaborative
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strong track record in programming, preferably in Python. The candidate should have demonstrable expertise in computational modelling of solids, preferably using density functional theory methods. Previous
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to assist other team members and contribute to a collaborative team environment. Knowledge of other programming languages like Python, Java or Swift a bonus. Experience of working in a regulatory environment
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to scientific problems, preferably with materials or polymer datasets. Proficiency in Python and scientific computing libraries; familiarity with machine-learning frameworks and data processing tools. Experience
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Bioinformatics, Plant Genomics or other related field appropriate to the goals of this project. Experience Demonstrable prior experience in bioinformatic analysis of RNA-seq data using R or Python is essential
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, materialized views, etc). • Experience of modelling in Data build tool (DBT). • Experience with the Python programming language or similar. • Experience with source control systems such as GIT
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/Word/PowerPoint/Databases etc). Demonstrable skills in programming language, such as R, Python, SQL, SAS or Matlab. Proven ability to negotiate and influence others. Demonstrate ability to handle several