Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- Lunds universitet
- Umeå universitet stipendiemodul
- KTH Royal Institute of Technology
- Umeå University
- University of Lund
- Uppsala universitet
- Karolinska Institutet (KI)
- Linköping University
- Nature Careers
- KTH
- SciLifeLab
- Swedish University of Agricultural Sciences
- Umeå universitet
- IFM, Linköping University
- Lulea University of Technology
- SLU
- Sveriges Lantbruksuniversitet
- Örebro University
- 9 more »
- « less
-
Field
-
and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational
-
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
-
-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
-
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
-
/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
-
Laue-Langevin (ILL), France, the International Institute of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work
-
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
-
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
-
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