Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Lunds universitet
- Linköping University
- SciLifeLab
- Blekinge Institute of Technology
- Jönköping University
- Karlstad University
- Karlstads universitet
- Uppsala universitet
- Göteborgs Universitet
- KTH
- Karolinska Institutet (KI)
- Kungliga Tekniska högskolan
- Stockholms universitet
- Swedish University of Agricultural Sciences
- Umeå University
- University of Borås
- University of Lund
- University of Skövde
- 10 more »
- « less
-
Field
-
experience in manufacturing systems modeling, simulation (i.e., DES), and digital twins. • Good knowledge and experience in machine learning, reinforcement learning, and AI-based optimization for production
-
generative machine learning models to create an active learning cycle to identify materials with adequate properties. Promising materials will be synthesized, characterized and evaluated in lab. This will help
-
to the forefront of quantum technology, and to build a Swedish quantum computer. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and
-
international academic institutions and 14 industry partners (https://euraxess.ec.europa.eu/jobs/401249 ). We work together in the field of fluid-structure interaction in technical systems and industrial
-
description The Department of Computing Science at Umeå University is looking for a doctoral student in machine learning for software security. The position is for four years of full-time doctoral studies
-
with catalysis/photochemistry Programming skills using Python and MATLAB Analysis of complex scientific data through machine learning What you will do Plan experiments together with your supervisor and
-
, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our dedicated webpage . About the research project We will recruit a
-
implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
-
networks Scientific programming for simulation, data analysis, and reproducible workflows (e.g., Python/Julia/Matlab/C++) Machine-learning–inspired methods for reservoir/neuromorphic computing and
-
will use advanced evaluation techniques, data mining, and generative machine learning models to create an active learning cycle to identify materials with adequate properties. Promising materials will be