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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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: Develop innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry
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the field of biotechnology, bio-/chemical engineering, (bio) process engineering, bioinformatics, biophysics or biomathematics. Ideally you have Programming skills and knowledge on machine learning and
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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positions (TV-L E13). Addressing global challenges, the school provides a wide variety of topics, from logic in autonomous cyber-physical systems to machine learning in Earth System models. You will have one
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defects of smectic-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this
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of computational approaches to large scale simulations Basic knowledge of (geo)chemical processes and machine learning will be of advantage Expertise in Machine Learning approaches, ideally beyond neural networks