Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Chalmers University of Technology
- Chalmers tekniska högskola
- SciLifeLab
- Linköping University
- Lunds universitet
- KTH Royal Institute of Technology
- Karlstad University
- Karlstads universitet
- University of Lund
- Luleå University of Technology
- Nature Careers
- Umeå University
- University of Gothenburg
- Karolinska Institutet (KI)
- Kungliga Tekniska högskolan
- Linkopings universitet
- Lulea University of Technology
- Uppsala universitet
- Chalmers University of Techonology
- Epishine
- Fureho AB
- Jönköping University
- KTH
- Mälardalen University
- Stockholms universitet
- Sveriges lantbruksuniversitet
- Swedish University of Agricultural Sciences
- Umeå universitet
- Uppsala University
- Örebro University
- 20 more »
- « less
-
Field
-
with researchers at Chalmers and the University of Gothenburg. You will explore how Bayesian methods can enable risk-aware, real-time trajectory planning and contribute to the development of autonomous
-
University of Gothenburg. You will explore how Bayesian methods can enable risk-aware, real-time trajectory planning and contribute to the development of autonomous vehicles that are both safe and trustworthy
-
standards. About the research project The postdoctoral project will focus on precision tests of low-energy strong interactions via the ab initio modeling of open-shell, nuclear many-body systems and Bayesian
-
Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
-
theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
-
presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
-
version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
-
-based, Bayesian or matrix factorization methods for multi-omics integration. Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
-
, or network-based, Bayesian or matrix factorization methods for multi-omics integration Ability to independently perform data analysis and scientific interpretation based on omics data at an internationally
-
that facilitate sustainable soil remediation and waste management. Project description The main objective of this project is to create a sustainable and cost-effective solution for managing PFAS-contaminated soil