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Postdoctoral Researcher - Proteomics Faculty: Faculty of Science Department: Department of Pharmaceutical Sciences Hours per week: 36 to 40 Application deadline: 6 May 2026 Apply now As a
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 20 days ago
acquisition Good communication skills in English and ability to collaborate in interdisciplinary teams Desirable qualifications Experience with machine learning methods for regression or signal interpretation
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related field • Strong quantitative background (e.g. ecological theory and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated
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Description VIB.AI, the VIB Center for AI & Computational Biology, is a young research center dedicated to combining machine learning with in-depth knowledge of biological processes. Our mission is
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a PostDoc who will do research on the intersection of machine learning (ML) and statistics
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meteorology, especially cloud microphysics, including the initiation and growth of ice particles; machine learning (e.g. neural networks). Consideration will also be given to good collaborative skills and drive
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML) methods to tackle complex biomedical challenges in
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–based, web-enabled decision-support tool for farmers, using machine learning (random forest) predictive models. Working closely with the PhD student and PIs to ensure rigorous model development