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); Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R; Strong quantitative skills and analytical reasoning applied to observational data; A track record
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Randomization, co-localisation); Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R; Strong quantitative skills and analytical reasoning applied
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simulators. Proficiency in Python, including data handling (pandas, NumPy), visualization (matplotlib) and integration within simulation workflows. Understanding of sector coupling (e.g. P2G, P2H), energy
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) Familiarity with using R and/or Python for answering biological questions (desired) WE OFFER: An international, multidisciplinary, and creative working environment Innovative technologies Excellent
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 1 month ago
interdisciplinary collaboration. Demonstrated programming skills, preferably in Python (e.g., PyTorch, NumPy, pandas, polars). Experience with spatial and/or temporal data analysis (geographic data, satellite imagery
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mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and experience with the Linux operating system
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experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and deployment
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of high-dimensional data (e.g., single-cell RNA-seq, phylogenetics, spatial data) Experience in R or Python Application instructions: Please send a CV, a brief cover letter describing your research
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essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential. Experience in MRI and/or EEG acquisition and analysis is desirable. Experience working on research
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.). Strong programming skills in Python, C/C++, and experience with data-centric control and monitoring architectures. Knowledge on standard communication protocols for smart grid interoperability, such as IEC