Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Linköping University
- Lunds universitet
- Umeå universitet
- KTH Royal Institute of Technology
- Karlstad University
- Karolinska Institutet (KI)
- Swedish University of Agricultural Sciences
- Umeå University
- Uppsala universitet
- Blekinge Institute of Technology
- Institutionen för akvatiska resurser
- KTH
- Karlstads universitet
- Linköpings universitet
- Luleå University of Technology
- Mälardalen University
- SciLifeLab
- Sveriges Lantbruksuniversitet
- Umeå universitet stipendiemodul
- University of Lund
- 11 more »
- « less
-
Field
-
on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
-
conduct research on the theoretical foundations of mathematical optimization, as well as its applications to emerging challenges in machine learning and engineering. You will write and submit research
-
computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
-
Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
-
on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
-
research area MERGE (https://www.merge.lu.se ), focused on climate modelling. Aerosol research has been conducted at Lund since the 1970s and is now a designated profile area at LTH (https://www.lth.se
-
that you will help us to build the sustainable companies and societies of the future. The research at the Division of Machine Elements is mainly focused on tribology and its applications. The research group
-
network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
-
– then these may be taken into consideration. We are looking for someone with a PhD in computer science or related areas. The candidate has a strong research record with publications in top-tier
-
at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models