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Field
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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architecture exploration, hardware/software co-design and operating/runtime systems. Typical application domains are e.g. signal-/image processing, artificial intelligence and machine learning. Tasks: research
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by Prof. Marcel Oliver, is part of the Mathematical Institute for Machine Learning and Data Science (MIDS) at the KU Eichstätt-Ingolstadt. The research group works at the intersection of analysis
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or Postdoctoral position (m/f/d) - Interpretable Machine Learning for Catalytic Reaction Network Discovery. A full-time PhD or Postdoctoral position is available in a collaborative Max Planck research
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collisions and maximize efficiency through innovative AI-based movement and maneuver planning. For the first time, innovative machine learning concepts, such as “shadow learning”, are being used. Appropriate
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PhD Student (gn*) Job Id: 11029 Limited to 3 years | Part-Time with 65% | Salary according to TV-L E13 | Institute of Molecular Tumor Biology We are UKM. We have a clear social mission and, with
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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, as well as the way memory, trauma and history are entangled. Job description: In close coordination with the project management and the other project members, the computer-assisted analysis
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macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data. Our group works on the definition and implementation of strategies