Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Nature Careers
- Empa
- Umeå University
- CNRS
- Centrale Supelec
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- Inria, the French national research institute for the digital sciences
- Leipzig University •
- Molde University College
- Monash University
- Queensland University of Technology
- Stockholms universitet
- UNIVERSITY OF VIENNA
- University of Twente
- University of Vienna
- Universität Wien
- 7 more »
- « less
-
Field
-
education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
-
more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By
-
theory to experiment, from basic research to more applied projects often focusing on sustainable chemistry but also advanced analytical methods of organic and inorganic chemicals and materials
-
for the first four full-time equivalent years of your doctoral studies. You will have the opportunity to work with leading national and international researchers – experts in social network theory, qualitative
-
, 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 activities
-
. FeelGoodAI will be applied to the field of education, where learner mental health and well-being are major concerns. Multiple perspectives and constructs related to well-being underpin several theories from
-
of biomolecules which can only be successfully tackled by employing a variety of different theoretical methods. In this respect, this joint graduate college brings together the expertise in analytical theory from
-
, which arise from processes such as hybridization, horizontal gene transfer, and recombination. Creating such networks from DNA sequences requires techniques from graph theory, theoretical computer science
-
ability to work both independently and collaboratively are essential. A solid background in combinatorics, especially graph theory, together with basic knowledge of probability theory, are required
-
processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep