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to shaping the future. Would you like to gain experience in a modern and professional team and see first-hand how research and development in the field of process optimization using probabilistic AI works
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ultra-reliable state estimation and control. With rising renewable energy integration, operators need certifiable performance guarantees to prevent blackouts while optimizing grid efficiency. Current
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products and processes. The Competence Center »Energy Management and Control« develops innovative solutions for the economically and technically optimal operation of highly complex energy systems. We develop
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various aspects of QC ranging from quantum chemistry to optimization and machine learning topics. We offer interested students to participate in this cutting-edge research via master thesis projects in
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« department, we deal with technologies, processes and methods for optimizing production processes as well as quality assurance in manufacturing. In the »AGILEHAND« research project funded by the European Union
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Master Thesis on Bayesian Optimization of Multi-stage Processes with Smart Inducing Point Allocation
to shaping the future. Would you like to gain experience in a modern and professional team and see first-hand how research and development in the field of process optimization using probabilistic AI works
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the integration of both Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs). This position will involve training and optimizing these neural networks using Python frameworks, including CUDA
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and design. One of the main focuses of applied research at Fraunhofer UMSICHT is the development and optimization of energy materials (such as compound-based bipolar plates) as well as cell and stack
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of various carbonisates Carrying out tests on existing pyrolysis plants including optimization of the process parameters Analyzing the resulting products in the laboratory using various analysis techniques
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using a numerical simulation model in Modelica/Dymola. We are optimizing the processes according to the given requirements. The aim is to develop a parametrizable universal model based on various created