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interdisciplinary thinking. Opportunities to learn and collaborate through Dr. Rodriguez’s affiliation with Yale’s Wu Tsai Institute . What you’ll do: Design and build a state-of-the-art deep-tissue imaging platform
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research groups focused on molecular biosensing for point-of-use applications. To learn more, visit MOBIUS website at https://mobius.org.au/ The primary objective of MOBIUS is to accelerate the growth
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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: The UC academic salary scales set the minimum pay determined by rank and step at appointment. See the following table for the current salary scale for this position: https://www.ucop.edu/academic-personnel
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development through emerging deep learning techniques is of strong interest. The candidate will also evaluate and integrate existing tools and databases into high-throughput pipelines, and facilitate
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quantitative, research-oriented postdoctoral scientist with strong AI/data science and statistical modeling expertise. The ideal candidate will have advanced coding skills and a deep interest in translating
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
PhD in computer science, mathematics, physics, bio-/medical informatics or related fields, specializing in image analysis or machine learning, proficiency in deep learning techniques (CNN, VIT
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image
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/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
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Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g