Sort by
Refine Your Search
-
Listed
-
Employer
- Lunds universitet
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Uppsala universitet
- Umeå University
- Karolinska Institutet (KI)
- Nature Careers
- SciLifeLab
- Umeå universitet
- Umeå universitet stipendiemodul
- University of Lund
- Chalmers tekniska högskola
- KTH
- Linköping University
- Örebro University
- IFM, Linköping University
- Karolinska Institutet
- Linköping university
- Linköpings universitet
- SLU
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- chalmers tekniska högskola
- 13 more »
- « less
-
Field
-
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with
-
mathematics to work with Axel Ringh on a project funded by the Swedish Research Council (VR). The project is centered around inverse optimal control/inverse reinforcement learning, both for continuous-time and
-
geometries. However, AM-generated surfaces exhibit significant and highly irregular roughness, a key factor that strongly modifies turbulence, pressure drop, and heat transfer. Unlike conventional machined
-
, development of chemical process solutions for repurposing of electrodes, and integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and
-
located at SciLifeLab in Stockholm. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in
-
will (micro-)benchmark Java-based applications using JMH. You will collect performance measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning
-
/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data
-
/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience
-
at the Department of Medical Biochemistry and Biophysics, which offers an international, collaborative, and open-minded research environment. Please visit the lab’s webpage for more information: https://www.umu.se/en
-
description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources