Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- KTH Royal Institute of Technology
- Nature Careers
- Chalmers tekniska högskola
- Uppsala universitet
- Chalmers te
- Karlstad University
- Karolinska Institutet (KI)
- Linköpings universitet
- Lunds universitet
- SciLifeLab
- Sveriges lantbruksuniversitet
- chalmers tekniska högskola
- 3 more »
- « less
-
Field
-
principles, and computer-based analysis methods. The research will include investigating aggregated data from genetics, archaeology and linguistics Requirements PhD degree in Population genetics
-
The postdoctoral researcher will work with computer-based analytical methods and large databases to develop theory and methodology for utilising aggregated data from archaeology, genetics, and linguistics, thereby
-
equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made. Demonstrated research expertise related to real-time computer graphics programming
-
in the Natural Sciences (AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians working collaboratively. Our focus is on developing practical methods
-
(starting date flexible). We develop state-of-the-art computational methods and take a leading role in our field. The successful candidate can therefore expect to contribute at the international forefront
-
estimators for quantifying the utility and the level of privacy protection provided in synthetic data. As a postdoctoral researcher in this position, you will have the opportunity to develop your creative and
-
23 Oct 2025 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Computer architecture Computer science » Other Environmental science » Earth
-
’ environments, raising concerns about their ability to evolve fast enough to avoid extinction. Whether species will persist or disappear is often unclear as existing methods to predict contemporary evolution work
-
machine learning with applications in science. The aim is to develop novel machine learning and other data driven AI methods for scaling up and improving scientific processes beyond what humans can do, for
-
bioinformatic methods to detect environmental adaptation. The methods will be tested using simulations of genomic data. The work consists of working in Uppsala University’s computer cluster as well as programming