Sort by
Refine Your Search
-
Country
-
Employer
- Monash University
- ;
- ETH Zurich
- Fraunhofer-Gesellschaft
- Nature Careers
- University of Glasgow
- Binghamton University
- Florida International University
- Forschungszentrum Jülich
- Freenome
- Grand Valley State University
- Institut Pasteur
- KINGS COLLEGE LONDON
- Nanyang Technological University
- Queensland University of Technology
- SUNY University at Buffalo
- SciLifeLab
- The Ohio State University
- The University of Chicago
- University of Bristol
- University of Houston Central Campus
- University of Oregon
- University of Oslo
- University of Sheffield
- University of Texas at Arlington
- University of Toronto
- 16 more »
- « less
-
Field
-
to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
-
expertise and interests in population sciences, Bayesian statistics, causal inference, statistical learning, high-dimensional data, and/or electronic medical records are encouraged to apply. Successful
-
be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high
-
networks, Bayesian inference, computational neuroscience, mathematics.
-
Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling and simulation techniques, prior distributions and posterior predictive checks, model comparison
-
wild and domestic animal populations wildlife diseases and conservation network analysis of disease spread phylodynamics model-based statistical inference using Bayesian approaches vector biology
-
nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or
-
back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968
-
Methods of balancing model complexity with goodness of fit include Akaike's information criterion (AIC), Schwarz's Bayesian information criterion (BIC), minimum description length (MDL) and minimum
-
the Faculty of Science. We will apply Bayesian approaches such as the information-theoretic minimum message length (MML) principle and other approaches to develop a path towards statistically-optimal algorithms