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algorithms. To strengthen our team, we are looking for a scientific staff member who specializes in the modeling and simulation of high-performance laser diodes used as pump sources for high-energy lasers in
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methods, machine learning algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems
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, forecasting, adaptive learning algorithms, and long memory, both from theoretical and applied perspectives. Empirical applications concentrate on macroeconomic, financial and climate data. Your mission
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for new quantum computing algorithms. It will rely on statistical structure learning represented by knowledge graphs and efficient low-rank tensor compressions. We are looking for: A completed scientific
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use dynamic simulations to carry out system studies that depict the future grid. To meet the resulting challenges, we develop new control algorithms and analyse their effect. These include, for example
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algorithms to analyze OMICS data (e.g., genome, transcriptome, proteome, microbiome) from patient samples and basic research perform single-cell RNA-Seq and spatial transcriptomics analysis apply artificial
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near-real-time forecast system for the Baltic Sea Generate high-resolution daily surface salinity maps for the Baltic Sea and validate them with available observational datasets Develop algorithms and
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”. The “Computational Methods” group focuses on the design and simulation of laser beam sources and laser processes, using numerical methods and modern artificial intelligence algorithms. To strengthen the team, we
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Research profile in machine learning (e.g. robustness, out-of-distribution/anomaly detection, fairness, explainability, uncertainty quantification) or AI applications in the healthcare domain Interest in
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data analysis and develop sophisticated mathematical models for simulating power system behaviors under various scenarios. Development and Testing: Design and develop control algorithms to enhance grid