Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Monash University
- University of Sheffield
- ETH Zurich
- Rutgers University
- University of North Carolina at Chapel Hill
- ;
- Columbia University
- Rice University
- SUNY University at Buffalo
- University of Glasgow
- Université d'Orléans
- Aston University
- Binghamton University
- Brown University
- CNRS
- Chalmers University of Technology
- Chalmers tekniska högskola
- Fermilab
- Florida State University
- Forschungszentrum Jülich
- Freenome
- Institut Pasteur
- KINGS COLLEGE LONDON
- NIST
- Nature Careers
- North Carolina State University
- SciLifeLab
- Syracuse University
- Temple University
- University of British Columbia
- University of Florida
- University of Leicester
- University of Manchester
- University of Massachusetts
- University of Miami
- University of Michigan
- University of South Carolina
- University of Texas at Austin
- University of Utah
- University of Warsaw
- Univerzita Karlova (Charles University)
- 31 more »
- « less
-
Field
-
silico model of normal development. Bayesian inference will calibrate model parameters and highlight control points, with predictive accuracy benchmarked against existing perturbation datasets. O3. Map
-
inference, analysis of high-dimensional and -omics data, Bayesian methods, and clinical trials, with active collaborations in cancer, aging, HIV, and the analysis of large-scale health data. The School
-
Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
-
Bayesian ML approaches for path inference; introducing sensors; behaviour classification; resource-constrained active-learning; other IoT applications; microbattery development and field experiments and
-
methodologies underlying spatial data, high-dimensional and big data (e.g., data from wearable devices, electronic health records), Bayesian statistics, and learning algorithms as novel data analytical tools in
-
of statistical analyses, in particular: Exploratory and confirmatory factor analysis, Multilevel analyses (including latent class analysis), Time series analysis, Bayesian inference methods, Regression techniques
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
are particularly interested in scholars who advance methodological frontiers, such as causal inference, complex systems modeling, implementation science, longitudinal or big-data analytics, community-engaged methods
-
). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty