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for various technologies and develop algorithms and software tools dedicated to accelerating research on multiple levels. We are working at the intersection of computer science, physics, and material science to
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extension to four years, will focus on leveraging the next generation of evolutionary algorithms to evolve efficient, robust, and interpretable predictors and data processing algorithms. A crucial aspect will
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24.06.2021, Wissenschaftliches Personal The Professorship of Data Science in Earth Observation (Prof. Xiaoxiang Zhu) is seeking a research associate in the market of AI for Earth observation in
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the Research Group “Stochastic Algorithms and Nonparametric Statistics” in the project „SFB/Transregio 388“ SFB/Transregio 388 investigates the interplay between rough analysis and stochastic dynamics. Central
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scientific work on design automation for quantum computers and develop methods and software tools dedicated to the design and realization of quantum algorithms/circuits. One of the main challenges in
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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic
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., based on the 1D or analytical model) Hybrid simulation approach (e.g., which combine CFD and 1D simulations) High Performance Computing and/or GPU programming for this domain Machine learning algorithms
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, storage and demand. YOUR TASKS You will develop mathematical models and metaheuristic algorithms for complex optimization problems in the context described above, see e.g., https://arxiv.org/abs/2503.01325
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. Defining predictive tasks based on clinical goals. Selecting and setting up appropriate data preprocessing pipelines. Training and evaluation of computer vision models. Internal and external algorithmic
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will need strong coding skills to design highly efficient algorithms. Solid knowledge in the areas of algorithmics, optimization problems, as well as experience with SAT/SMT solvers or machine learning