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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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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine learning for improving
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from chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas
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, materials science, or a closely related discipline Apply: https://mgician.eu/research/doctoral-candidate-projects/dc1/ DC2: Synthesis and Transport Studies of Magnesium-Based Thermoelectric Materials Host
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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Developing new machine learning
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offers the chance to obtain further academic qualification (usually PhD). Tasks: The Chair of "Physics of Quantum Materials" (https://tu-dresden.de/mn/physik/ifmp/pdqm ) is seeking a motivated researcher
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offers the chance to obtain further academic qualification (usually PhD). Tasks: The Chair of "Physics of Quantum Materials" (https://tu-dresden.de/mn/physik/ifmp/pdqm ) is seeking a motivated researcher
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, public authorities in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will pioneer