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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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mathematical modeling to simulate water fluxes and biogeochemical processes related to carbon and nitrogen cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset
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contributions. Bremen is an ideal study area because it includes urban, industrial, and semi-rural areas, surrounded by agriculturally intensive land. These conditions result in a mixture of emissions from both
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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
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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) in materials science, physics, chemistry, electrical engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials
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and transfer of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and