Sort by
Refine Your Search
-
Category
-
Employer
- Forschungszentrum Jülich
- Technical University of Munich
- DAAD
- Brandenburg University of Technology Cottbus-Senftenberg •
- Leibniz
- RWTH Aachen University
- Academic Europe
- Carl von Ossietzky University of Oldenburg •
- Deutsches Elektronen-Synchrotron DESY •
- Dresden University of Technology •
- Goethe University Frankfurt •
- Julius-Maximilians-Universität Würzburg •
- Ludwig-Maximilians-Universität München •
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Medical Research, Heidelberg
- Technical University of Darmstadt •
- The Max Planck Institute for Neurobiology of Behavior – caesar •
- University of Bremen •
- University of Göttingen •
- University of Kassel •
- University of Mannheim •
- University of Münster •
- University of Potsdam •
- Universität Hamburg •
- 14 more »
- « less
-
Field
-
Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: Join an interdisciplinary team that brings state-of-the-art AI
-
of expertise in fundamental research on condensed matter, ranging from topics such as Quantum Materials / Electronic Structure & Quantum Many-Body Theory / Nanoscience / Solid State Spectroscopy / Solid State
-
the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
-
and Computer Science, Medicine, Philosophy, Physics and Astronomy, Law, and Management and Economics. Renowned for its strong scientific profile, the University of Würzburg is part of the U15 group
-
of Economics and Management Faculty of Mathematics and Natural Sciences Faculty of Organic Agricultural Sciences Faculty of Civil and Environmental Engineering Faculty of Mechanical Engineering Faculty
-
challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials design are often too slow, costly, and inefficient to cope with the increasing complexity of performance and
-
challenges in energy, mobility, and sustainability. Traditional trial-and-error methods in materials design are often too slow, costly, and inefficient to cope with the increasing complexity of performance and
-
consistent scientific methods for mobility planning and management, (2) integrate a new set of modular metrics for responsible mobility, (3) embed the planning methods into the open data AgiMo Digital Twin, (4
-
approach, REUNATECH supports the European Green Deal, Sendai Frame-work, and UN Sustainable Development Goals. DCs will benefit from an immersive training structure comprising cross-sectoral secondments
-
science and information science techniques. Several areas of computer science and mathematics play important roles: data management and engineering, machine learning and data analytics, signal and image