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or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition-process-structure-property
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characteristics. What you bring to the table Academic Background: You hold a Bachelor's degree in Engineering, Material Science, Chemistry, Nanotechnology, Physics, Biology, or a related field. Technical Skills
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to the table You are a student in chemical engineering, chemistry, process engineering or a comparable field of study You have solid knowledge in catalysis and reaction kinetics Laboratory experience in
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 11 days ago
Job Code: 10-2025 Job Offer from July 21, 2025 The Max Planck Institute for Nuclear Physics is one of 84 research institutes of the Max Planck Society for the Advancement of Science e.V. (MPG
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the results, documenting the results, and summarizing the findings in a final report Your Profile: Enrolled in a Master’s program in chemistry, materials science, electrochemistry, or a related field Experience
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following areas: Mathematical Analysis/ Numerical Analysis/ Theoretical Machine Learning Please note: Applications from candidates with degrees in other disciplines (e.g., Computer Science, Engineering) will
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(TIB ) – Leibniz Information Centre for Science and Technology – Program Area D, Open Research Knowledge Graph, is looking to employ a Research Software Engineer for Ontologies and Knowledge Graphs (m/f
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, specifically pertaining to the university’s master's program in mathematical physics and to undergraduate mathematical education, as well as moderate responsibilities in academic self-administration such as
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Degree PhD Course location Potsdam In cooperation with The innovative PhD and postdoctoral programme is offered in conjunction with the Faculty of Digital Engineering, a faculty jointly founded by
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did