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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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dissertation within the due time of 3 years and 9 months. Successful candidates will work together with approx. 3 doctoral researchers and 5 post-doctoral researchers at the Chair of Transport Modelling and
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, remanufacturing, repurposing and recycling to each other for the realization of an agile network. Various machine learning approaches will be used here. Your tasks are: Requirements definition, survey and
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: university and, if applicable, PhD degree (e.g. Master/Diploma) in mathematics, physics, materials science or related subjects basic knowledge of computer programming (e.g. Python, Matlab and C++) excellent
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, their achievements and productivity to the success of the whole institution. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Robotics offers a full-time
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science. A wide range of quantum theoretical methods shall be employed. A solid background in quantum mechanics and programming skills are prerequisite for this position, as is the readiness to learn and to
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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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