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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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methods, including coarse-grained and atomistic molecular dynamics, systematic coarse-graining, machine learning, and continuum solvers for hydrodynamics, such as the lattice-Boltzmann method. Among other
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modern models beyond the worst case e.g. integrating machine learning into algorithm design. We are looking for candidates with a strong mathematical background, an excellent degree in mathematics
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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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responsible 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
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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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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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new approach for microelectronics technology, this project teams up materialists, electrical engineers, and computer scientists of Forschungszentrum Jülich, TU Dresden University, RWTH Aachen University