Sort by
Refine Your Search
-
of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
-
a spatially explicit predictive model for Everglades vegetation dynamics in response to major drivers. The major objectives are to explore the distribution models that discriminate among prairie and
-
functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
-
can be tackled. A video describing the project can be viewed here: https://www.youtube.com/watch?v=IzPuuBnrIDc . The successful candidate will be developing Bayesian models for estimating
-
an inclusive community that is respectful and fair for all. Find out more about the benefits of working at the University and what it is like to live and work in the Durham area on our Why Join Us
-
closed-world assumption to incorporating open-world perception and action capabilities. The primary objectives of your project/research include: Enabling robots to detect known objects while identifying
-
, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world
-
appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
-
); mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is