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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we
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to extract knowledge from data, modelling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but are not limited to models and algorithms for knowledge
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algorithms are agnostic of the downstream task they will be deployed on, and this may lead to a suboptimal control performance. In this project, we will investigate control-oriented biases and their impact on
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international conferences or journals, especially publications in the area of Air Traffic Management Knowledge of optimization techniques Knowledge in the area of design and analysis of algorithms Great emphasis
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to the fundamentals and algorithms of spatially and time-multiplexed oscillator-network computing. Duties The PhD student will focus on the fundamentals and algorithms for spatially and time-multiplexed oscillator
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evaluating efficient and scalable techniques for systems that process and answer such queries (e.g., query optimization algorithms, adaptive query processing approaches). Conducting this research work includes
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. This involves formulation, implementation, and validation of novel hybrid models. The study emphasizes methodological innovation, scalable algorithms, and translation to industrially relevant multiphase reactors
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for fast tune-up Implementation and benchmarking of quantum algorithms Qualifications We are seeking candidates with: A PhD in Physics, Applied Physics, Nanotechnology, Computer Science, Engineering, or a
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is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view
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, epidemiological data and outcome modelling using AI-assisted algorithms, as well as multi-modal data integrations. An established data infrastructure with expertise and computational pipelines for these analyses