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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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systems, statistical physics and machine learning, and using these insights to develop new methods, with the support of competent and friendly colleagues in an international environment? Are you looking
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future
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) and Machine Learning/NLP (Natural Language Processing) to capture both the network embeddedness and the qualitative B2B relationship features of supply chains. The project identifies key bottlenecks
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to machine learning is well funded and continuously publishes in high impact journals. We foster a creative working environment, where you will find freedom to implement, develop, and publish research
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. Previous experience with machine learning applications in molecular modelling, including experience with at least three of the following Python libraries: TensorFlow, PyTorch, JAX, RDKit. Previous
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Python) and data analysis or machine learning applied to materials science Ability to work in interdisciplinary project or industrial experience About the employment The employment is a temporary position