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research school on secure distributed computing (SeDiC) is proposed. SeDiC aims to tackle the challenges of exchanging and computing data across a network of interconnected systems. It addresses scalability
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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, safe and socially just energy supply system based on renewable energies. We contribute to this with our research priorities of energy supply, energy distribution, energy storage and energy use. We
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, safe and socially just energy supply system based on renewable energies. We contribute to this with our research priorities of energy supply, energy distribution, energy storage and energy use. We
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? You have some experience in research and are familiar with maritime navigation or optimization algorithms? Great! You also have solid Python skills and enjoy working analytically and documenting your
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an extensive safety analysis and calidation of perception algorithms in automotive. Through our work, we lay the foundation for a reliable digital future. What you will do In our Trustworthy Digital Health group
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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assistance systems Collaboration in the development of AI algorithms (LLM, fine-tuning, RAG, AI agents, embeddings) Literature research on the topic of AI What you bring to the table Studies in
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, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable
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. Particularly, we would like to address mixed-phase processes in deep convective clouds to understand how these clouds interact with aerosol: How do deep convective clouds affect the aerosol size distribution and