Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- SciLifeLab
- University of Groningen
- ; The University of Manchester
- ; University of Oxford
- Chalmers University of Technology
- Curtin University
- DAAD
- Ghent University
- Mid Sweden University
- NTNU - Norwegian University of Science and Technology
- Technical University of Denmark
- Technical University of Munich
- Umeå University
- University of Adelaide
- University of Copenhagen
- University of Nebraska–Lincoln
- 6 more »
- « less
-
Field
-
to the PhD project ie. processing and analysis of dietary intake data, statistical analyses (eg. linear mixed models) as well as evaluation of child growth and body composition data. Relevant publications
-
-in time of new infrastructure is years, if not decades. The combination of conventional linear optimization energy models, which cover for the major part of the system, and the inclusion of partial
-
the effects of local policies such as building refurbishment strategies, and examining the role of hydrogen in facilitating sustainable energy transitions in cities. Join our dynamic institute to tackle climate
-
are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
-
the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
-
, and contribute to identifying tumor vulnerabilities that may become future therapeutic targets. What we offer: A dynamic and interdisciplinary research team with expertise in cancer biology, statistics
-
structures, etc to solve challenging problems is required (there will be a practical coding assessment during recruitment) A solid mathematical foundation is required (multivariable calculus, linear algebra
-
understanding of deep neural networks by exploring the human-understandable meanings of learnt features, the evolutionary dynamics of these features across network layers, and the architectural designs