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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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platform for cancer, in collaboration with experimental partners. Your tasks: Development and application of interpretable large-scale hybrid mechanistic- and machine learning-based mathematical models with
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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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positions (TV-L E13). Addressing global challenges, the school provides a wide variety of topics, from logic in autonomous cyber-physical systems to machine learning in Earth System models. You will have one
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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(in the Erlangen-Nuremberg area): 325 EUR (250 to 600 EUR) Food: 168 EUR Clothing: 42 EUR Transport (public transport and/or car): 94 EUR Learning materials (depending on the subject): 20 EUR Health
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engineering (or a similar discipline) with focus on sensorics; experience in data processing and machine learning; experience in 2D materials synthesis and device fabrication; knowledge in neuromorphic
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Max Planck Institute for the Study of Societies • | Koln, Nordrhein Westfalen | Germany | about 3 hours ago
. Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme Yes Description/content The International Max Planck Research School on the Social
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parameter space of the electrolysis processes. DoE is required for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map