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/transport checks; maintain reproducible pipelines (configs, seeds, provenance). Collaboration and reporting: Work with the PI and team, contribute clean, well-tested Python code, document results, and prepare
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, Organization of field trips, data collection and lab work, Spectral data analysis, data processing, and model development, R’ or Python programming, Co-supervise PhD and undergraduate students. Be willing to be
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or advanced statistical models. Solid experience in advanced programming in R and Python is also required. Applicants must have expertise in the following tools: - Git/GitHub; - API development; - IDEs such as
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 17 hours ago
related field by the date of hire; strong background in ocean dynamics, climate modeling, and computational analysis; proficiency in Julia (or Python) programming; experience with ocean models (e.g., MITgcm
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the development of new use cases in collaboration with the Virome@tlas consortium, ShapeMed@Lyon and the FR BioEEnviS partners to promote One Health and global-scale approaches. Job profile Expected skills: Python
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functional theory) and high-performance computing. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to
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programming languages such as Python, Fortran, C, C++, etc.; statistical physics of liquids, etc.). A good knowledge of spoken and written English is also required. Website for additional job details https
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, execution, and regulatory submissions) proficiency in bioinformatics, statistical analysis (R, Python, SAS, or similar) fluent in English (written and spoken) proven grant-writing success and ability
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, transformer-based models), including experience with their application to biomedical and biological data; - Experience with machine learning frameworks and programming languages (e.g. Python) for handling large
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statistics and/or machine learning Specific knowledge • Proficiency in scientific computing • Knowledge of machine learning packages in Python or R • Proficiency in English (minimum level B2), as the postdoc