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Field
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cardiovascular care. Within the consortium, TU Delft contributes expertise in cardiac mechanics, soft tissue modeling, growth and remodeling, machine learning, and uncertainty-aware model personalization. As a
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modern deep learning frameworks (PyTorch, JAX, or equivalent). Have good software engineering habits — modular, well-documented, reproducible code. Are comfortable working in interdisciplinary teams and
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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. b (postdoc) Limited contract until: 14.10.2032 Job ID: 5906 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us
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. Ambition to learn how to do research at an increasingly higher level, including how to design high-quality experiments, interpret results, and answer scientific questions. Deep curiosity, reliability, and
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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
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in higher adelic analysis and geometry, applications of algebraic K-theory in arithmetic geometry, in anabelian geometry and IUT, in Diophantine geometry, in applications of modern mathematics to deep
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Engineering » Chemical engineering Technology » Environmental technology Computer science » Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 7 Sep 2026 - 23
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years prior to the application deadline. Experience with machine learning for scientific applications. Experience with deep learning frameworks such as PyTorch or TensorFlow. Experience with atomistic
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applications in image analysis and machine learning, as well as in digital signal processing and acoustic imaging. There are about 20 Postdocs and PhD research fellows in the group with financing from a variety