135 computer-science-intern "https:" "https:" "https:" "https:" "The University of Edinburgh" uni jobs at DAAD
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Description The joint CiM-IMPRS graduate program of the International Max Planck Research School - Molecular Biomedicine and Münster’s Cells in Motion Interfaculty Centre offers positions to pursue
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programs, the focus lies on individual research projects, complemented by program specific seminars as well as an individualized curriculum comprising lab and soft-skill courses, congresses, symposia and
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in a larger research team and involves exchange visits with the partner universities in Prague and Vienna. Requirements: university degree in mathematics or computer science strong background in
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supported by career development measures including at least one international research stay. For TUD Dresden University of Technology (TUD) diversity is an essential feature and a quality criterion of an
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degree in physics, informatics/computer science, electrical engineering or chemical process engineering. With more than 300 scientists, KCETA combines the research activities of the Karlsruhe Institute
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of the whole institution. The Cluster of Excellence REC² offers (subject to the availability of resources) at the Faculty of Electrical and Computer Engineering, Institute of Semiconductors and Microsystem (IHM
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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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journals Presenting results at scientific meetings Your profile Completed university studies (Master/Diploma) in the field of Chemistry, Physics, Computer Science or related field Knowledge of Density
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of industrial processes. In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties