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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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objective of the research group ‘Crop Physiology’ is to understand the physiology of plants down to the structure and function of genes and proteins as well as relevant mechanisms, which allow optimizing
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analysis, simulation, chemometrics, control engineering, artificial intelligence, soft sensors, and process sensors. We are always looking for new technologies and new methods to monitor and optimize
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04.04.2025, Wissenschaftliches Personal The Chair for Computer Architecture and Parallel Systems (CAPS) offers this position as part of the DARE-project funded by the EuroHPC JU bringing together
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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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(MUCCnet: atmosphere.ei.tum.de ) Optimization of an urban sensor network configuration for greenhouse gas and air pollutant measurements using mathematical and physical assessments Analysis of ground-based
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of the research group ‘Crop Physiology’ is to understand the physiology of plants down to the structure and function of genes and proteins. Thereby, relevant mechanisms are identified, which allow optimizing
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graph processing frameworks, graph databases, graph learning frameworks, knowledge graph systems, and graph mining systems. While each of them is specialized on a specific type of task and is highly
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, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since