Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
of our key research directions relevant for this position. Candidates should have (or be near to completing) a PhD in Computer Science or a related subject, or relevant experience. A high degree of
-
Biology. If you have not yet completed your master program, you can apply to be enrolled in our 4+4 PhD program, which is a way to commence the PhD study whilst concurrently completing the master program
-
– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
-
, Information Technology and Electrical Engineering Faculty (Steve Völler), Department of Architecture and Technology (Prof. Arild Gustavsen) About the project The PhD project is related to development of a new battery
-
expected. A PhD training programme is part of the agreement and the successful candidate will be enrolled in the Graduate School of Science and Engineering and in the Research School for Behavioral and
-
Research Studentship in ‘Deformation and fracture of TRISO fuel particles’ 3.5-year DPhil studentship Supervisor: Prof Dong Liu, Prof Emilio Martinez-Paneda About the Project The proposed PhD
-
of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Dr. Lei Yang and Prof. Johannes Kabisch (Norwegian University of Science
-
Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
-
academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
-
, and energy solutions. By integrating electrochemistry with advanced materials and engineering, the unit delivers pioneering solutions with real-world impact. Led by Prof. María Cuartero (ERC Fellow) and