Sort by
Refine Your Search
-
Country
-
Program
-
Employer
- University of Oslo
- University of Minnesota
- Chalmers University of Technology
- Cornell University
- Curtin University
- Duke University
- Harvard University
- Indiana University
- Lunds universitet
- Queen's University
- RIKEN
- Swansea University
- The University of Arizona
- The University of Iowa
- Umeå universitet
- University of Basel
- University of Connecticut
- University of Nottingham
- Zintellect
- 9 more »
- « less
-
Field
-
to characterize developmental patterns from multi-omics data Developing and parameterizing mechanistic mathematical models describing microbiome-immune dynamics Applying Bayesian inference and model fitting
-
, mostly tailored to the case of dynamic sequential inference and probabilistic recommender systems. The position is connected to the project “Bayesian Rank-based unsupervised Integration of multi-source
-
of Bayesian estimation theory, stochastic processes, and statistical inference. Proficiency in scientific programming (Python, MATLAB, C++) and software engineering best practices (Git, testing, documentation
-
., reinforcement learning, drift-diffusion models, Bayesian inference). Background in affective neuroscience, cognitive control, or computational psychiatry. Familiarity with pharmacological or neuromodulation
-
inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
-
entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
-
relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
-
expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc. We work on a variety of learning problems, especially those involving supervised
-
experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
-
specimens to estimate historical age structures over the last 150 years. Forecasting Shifts in the Pollination Service Window. The researcher will use Bayesian inference (e.g., Integrated Nested Laplace