Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- SciLifeLab
- Chalmers University of Technology
- Linköping University
- Umeå University
- Uppsala universitet
- University of Lund
- Karolinska Institutet
- Lulea University of Technology
- Mälardalen University
- Swedish University of Agricultural Sciences
- Blekinge Institute of Technology
- Lunds universitet
- 2 more »
- « less
-
Field
-
of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
-
models and running simulations for various operational scenarios, but also applying the simulation results to the design and verification of biological shielding components, preparing risk analysis reports
-
Meritorious are: Education related to mathematics or statistics Experience in skin tumor diagnostics Experience in developing mathematical models Previous experience working in a clinical environment
-
multiagent dynamics, with special focus on human decisions and opinion dynamics. The research will deal with both theoretical and computational aspects. The student will develop dynamical models and apply them
-
competence in System Analysis including Environmental Systems Analysis and LCA, as well as Biometrics (statistics and mathematics with applications in biological systems) and Automation and Logistics. Read
-
research activities in stormwater management. The research is both theoretical and experimental with elements of computational technology and mathematical modelling and is based on close collaboration with
-
study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The research group Our lab is advancing precision medicine through deep learning models
-
! About the project Machine learning methods typically can only solve the tasks that they have been specifically trained to solve. They first adapt (train) a mathematical model on a number of examples and
-
The research in Theme A provides opportunities to address issues on measuring, assessing, and modelling of Quality of User experience (QUX). This includes personalizing QUX in novel intelligent realities
-
vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid