Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- KTH Royal Institute of Technology
- Chalmers University of Technology
- Karolinska Institutet (KI)
- Lunds universitet
- University of Lund
- Chalmers tekniska högskola
- Nature Careers
- SciLifeLab
- Umeå University
- Uppsala universitet
- Umeå universitet
- Umeå universitet stipendiemodul
- Göteborg Universitet
- Karolinska Institutet
- Linköping University
- Linköpings universitet
- Linneuniversitetet
- Sveriges Lantbruksuniversitet
- chalmers tekniska högskola
- 9 more »
- « less
-
Field
-
the Job related to staff position within a Research Infrastructure? No Offer Description Job description The work involves simulations of the dynamic vehicle-track interaction for various types of rail
-
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
-
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
-
appropriate treatment—ultimately saving lives. We are particularly looking for applicants with experience in prediction models and biomarker evaluation, causal inference, longitudinal methods, survival analysis
-
regulators of disease onset and progression. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating
-
. Responsibilities include processing large-scale sequencing data, developing and benchmarking methods for splicing and regulatory network inference, integrating multimodal data with clinical information
-
care gaps: Evaluating disparities in clinical management, assessing their impact and evaluating targeted interventions to improve them. Optimizing knowledge of treatment effects: Using causal inference
-
to floating-point arithmetic. Possible research directions include developing new automated program verification techniques specifically for such programs, as well as specification inference and fault
-
. Optimizing knowledge of treatment effects: Using causal inference methods to rigorously assess the safety and effectiveness of medications in real-world patient populations. Defining individualized treatment
-
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