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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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within physically sensible design spaces avoiding the need to learn every pathological flow scenario and making machine learning both efficient and reliable. The ultimate goal is to retain the fidelity and
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with, enhance or replace established methods
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subject from a recognised institution. A background in at least one of the following: Dynamical systems Photonics/Electromagnetics theory, design and simulations Machine learning mathematics and algorithms
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respectively, we seek highly motivated candidates to develop innovative methodologies for radar sounder data processing, with a strong emphasis on artificial intelligence and deep learning. The research activity
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Andres Floto and his group members. Key responsibilities include working on deep learning, deep generative modelling, and molecular design. Additional responsibilities include developing research
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of their behavior. A particular focus of the thesis will be on the design and monitoring of collective systems populated by AI generative agents, and on improving their theory of mind, lifelong learning and social