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Your Job: The accelerated development of advanced materials is essential for addressing major challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials
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(Methods for Active Informed Machine Learning). This project is a close collaboration with the Hasso Plattner Institute. We are developing and improving machine learning methods by integrating domain
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you will do Driving innovative AI research through the development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine
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The »High-Performance Cutting « department develops technologies and application-oriented solutions for machining along the entire process chain - from process design and process simulation to real
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 14 hours ago
installed ‘Brain Algorithms and Circuits’ research group of Dr. Gregor Schuhknecht is searching for a Doctoral Student (m/f/d). About the lab Our research interest is to investigate how the synaptic circuitry
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 14 hours ago
installed ‘Brain Algorithms and Circuits’ research group of Dr. Gregor Schuhknecht is searching for a Postdoctoral Researcher (m/f/d). About the lab Our research interest is to investigate how the synaptic
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
31.07.2025, Wissenschaftliches Personal The Chair for Efficient Algorithms, led by Prof. Stephen Kobourov, is inviting applications for a fully funded PhD position at the Technical University
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and digitization. More than 400 employees – including around 100 students – from over 50 countries work at nine locations in scientific and non-scientific teams on the development of innovative methods
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project within the SusMax network focused on developing interpretable machine-learning frameworks for kinetic multiphase reaction-network discovery in the catalytic conversion of renewable feedstocks