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small enough to offer a personal and engaging learning experience. In this way, we contribute every day to a safer and more sustainable world. Impact with law! For more information, please visit here
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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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; · is open for discussions and is eager to learn new ideas, techniques and approaches We encourage women, minorities and underrepresented groups to apply. The position of Postdoctoral Researcher in Leiden
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January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for postdocs; multiple courses on topics such as leadership for academic staff; multiple courses
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full projects, followed by the analysis, interpretation and reporting of data and results. You will pro-actively promote proteomics services and acquire new collaborative projects. The projects will vary
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on their qualifications, skills, and potential. Your background, gender, beliefs, or origin don’t matter. Candidates should be able to demonstrate motivation, eagerness to learn, and ability to work both independently and
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flexible and driven researcher with a (recent) PhD, preferably in virology, veterinary sciences, or a related biomedical field. You are accustomed to working in an interdisciplinary team, learn quickly and
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering
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extensive knowledge on zooplankton imaging techniques ability to program and train machine learning models for automated image classification experience with shipborne campaigns and ready to join multi-week
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning