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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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cryosphere and is involved in various high-level research projects and centers. (See for instance http://www.mn.uio.no/geo/english/about/organisation/geohyd and https://www.mn.uio.no/geo/english/research
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. Experience with deep learning and programming, preferably in Python, are required and should be evident from your academic track record, including the (online) courses you've followed, your publications
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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Position Description The Czech Technical University in Prague offers a Postdoctoral Fellowship Programme, full details can be found at https://international.cvut.cz/jobs-at-ctu/crop-postdoctoral
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | about 2 months ago
Minimum: USD $62,232.00/Yr. Salary Maximum: USD $75,564.00/Yr. GENERAL SUMMARY OF POSITION: Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display
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Memorial Sloan-Kettering Cancer Center | New York City, New York | United States | about 2 months ago
Experience in high-dimensional data analysis Preferably have a deep learning or foundation models for biomedical data. Submit C.V., cover letter, and 3 references in your application submission to: Contact
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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together to develop solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/postdoctoral-position-in-attos… Requirements Research
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics