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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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of AI and machine learning methods for advanced modelling and analysis of energy and industrial processes Experience with high-temperature processes, particularly in metal and mineral processing
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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learning can improve software architecture recovery, how to optimize machine learning models at compile and runtime, and autonomous agents for software development. Part of the research is conducted through
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new methods incorporating transformer models, graph neural networks, and self-supervised learning approaches that can extract deeper biological insights from genomic data. Join us in this exciting
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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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multiplex analysis. We will assist the computer scientists to apply artificial intelligence Machine Deep Learning models using the omics data of mitophagy to predict risk of cancer and metastasis and design
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and