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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
inform observable macroscopic properties as part of the activities of the UNC Superfund Research Program (SRP) (https://sph.unc.edu/superfund-pages/srp/). This work involves running, developing, and
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with or commitment to learning and growing as an instructor; experience using R or Python, either in teaching or to support work in statistics and/or data science. Desired Qualifications Experience
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language such as Python or Node.js) would be a plus. The candidate must be able to implement production-ready solutions in adherence to strict timelines, ensuring that deadlines are met, and that
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scientific imaging, as well as access to hyperspectral imaging equipment and to heritage collections studied within the laboratory's activities. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UAR3224
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interacting with different departments and external companies, fluency in English and in French is necessary. Good knowledge in LabVIEW or Python is a plus. Website for additional job details https
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recrystallization and control nucleation for biomedical applications in e.g. heart and kidney cryopreservation. Where to apply Website https://www.academictransfer.com/en/jobs/359905/phd-in-de-novo-design-of-ice-bi
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and generative protein design (e.g. AlphaFold, OpenFold, Boltz, Chai). Develop robust, reproducible and reusable Python code for model training, inference, and large‑scale computational experiments. Run
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competitive salary and access to state-of-the-art research infrastructure. Applications will be reviewed on a rolling basis until the position is filled. Previous Work https://ieeexplore.ieee.org/document
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or equivalent Skills/Qualifications Conocimientos de programación (Python). Empleo de herramientas de modelización numérica geotécnica (FLAC, PFC). Specific Requirements Conocimientos en Ingeniería Civil
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. • Strong proficiency in Python and relevant libraries for data analysis and modelling (e.g., TensorFlow, Keras, Scikit-learn, Pandas); knowledge of R would be an asset. • Familiarity with geospatial data