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data. Writing new R packages or Shiny Apps for implementation of developed methodology. Applying AI and ML tools (including Python, R, and possibly other languages) to test and evaluate biomedical
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strongly preferred. Strong Python skills, and proficiency in PyTorch and/or JAX. Ability to reason about neural network behavior from first principles: how architectural choices, regularization, and training
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, Mobility or Climate, among others. Strong programming skills in Python/R, machine learning frameworks, and dashboarding tools (e.g., Streamlit, Superset, Grafana, PowerBI). Familiarity with various types
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Cardioembolic Stroke Risk Stratification using AI Accelerated Patient-Specific Blood Flow Simulation
to work in the medical field • Good coding skills in Python • Fluent in English (Reading, Writing, Speaking) Contact People Send a CV and motivation leder to: maxime.sermesant@inria.fr, nadjia.kachenoura
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or more of the following FastAPI, PostgreSQL and/or asynchronous workflows (Apache Airflow, Celery, RabbitMQ). 4. Significant experience in Python and experience using common software development
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research, research data management and data quality control Demonstrable computer programming skills are essential, with good knowledge of CLI and Python/R Proven experience using REDCap for the design
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) or of bioinformatics (programming skills, R or Python, statistics, analysis of complex datasets) Ability to work independently and to take initiative, as well as teamwork skills German language skills at B1 level are
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of image processing methods on biological datasets, with an emphasis on microscopy. Proficiency in at least one programming language (Python, Java, ImageJ macro, R, Matlab) Familiarity with at least one
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of micropollutants Good basics in hydrological and substance flow modelling Experience in statistical data evaluation and programming skills (R, Python, etc.) Language skills Fluency in English and French or German
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scenarios. Develop and maintain production-quality software (primarily Python) to enable AI-assisted research processes across computational biology. Prototype, benchmark, and iteratively improve agentic