Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- Ghent University
- Technical University of Denmark
- University of Stuttgart •
- Vrije Universiteit Brussel
- Abertay University
- Chalmers University of Technology
- Cranfield University
- DAAD
- Forschungszentrum Jülich
- Heidelberg University •
- Leibniz
- NTNU - Norwegian University of Science and Technology
- University of North Carolina at Chapel Hill
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; University of Nottingham
- ; University of Sussex
- ; University of Warwick
- ; Xi'an Jiaotong - Liverpool University
- Curtin University
- Fraunhofer-Gesellschaft
- Goethe University Frankfurt •
- Hannover Medical School •
- KNAW
- RMIT University
- SciLifeLab
- Technical University of Munich
- The University of Iowa
- Umeå University
- University of Adelaide
- University of Bamberg •
- University of Bremen •
- University of British Columbia
- University of Groningen
- University of Konstanz •
- University of Newcastle
- University of Nottingham
- University of Twente
- Utrecht University
- WIAS Berlin
- 30 more »
- « less
-
Field
-
(i.e. red agents). However, due to a fragmented market, rapid technical developments, and nascent research the extent of capabilities and optimal solution architectures are not well understood. Current
-
for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
-
with privacy by developing techniques that optimize both aspects. The candidate will perform the work together with a team of postdoctoral researchers who are experts on the field and other PhD student
-
Optimized Design and Control of Soft Aerial Manipulators), in collaboration with INRIA Lille Nord-Europe in France and its leading Defrost team in soft robot simulation and control, see https
-
knowledge of energy system modelling or climate modelling Good knowledge of deep learning, PDEs or mathematical/numerical optimization methods Enthusiasm for challenging problems and interdisciplinary
-
seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high-performance electric propulsion systems. Funding 3-year PhD tuition fee (for UK home
-
alloys), and additive manufacturing to push performance boundaries. The research will seek optimal trade-offs between compactness and performance, delivering foundational insights into the future of high
-
networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
-
tools (e.g., drones, 3D mapping) for high-resolution geological mapping and rock mass quality assessment. Develop and calibrate numerical models using field data and case studies to simulate various
-
linkages based on numerical simulations and to transform them into AI- and ML-ready information to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit