Sort by
Refine Your Search
-
Country
-
Employer
- ; University of Sheffield
- ; University of Warwick
- RMIT University
- University of Bergen
- University of Groningen
- ;
- ; Max Planck Institute for Psycholinguistics
- ; Swansea University
- ; University of Exeter
- ; University of Reading
- ; University of Southampton
- ; University of Sussex
- DAAD
- Institut Pasteur
- Ludwig-Maximilians-Universität München •
- Nature Careers
- Technical University of Denmark
- Technical University of Munich
- University of British Columbia
- University of Oslo
- 10 more »
- « less
-
Field
-
ethnomycology or ethnobiology large-scale (ethnographic) database construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in
-
and/or Python. Experience in, and aptitude for, complex statistical modelling (inc. mixed effects regression models and/or Bayesian statistics). Excellent written and spoken English. Desirable (traits
-
main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
-
challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
-
Doucette Doctor of Philosophy in Physics (PhD) Probing Brain Tissue Microstructure with Magnetic Resonance Imaging through Bayesian Learning of Signal Dynamics Alexa Norton Doctor of Philosophy in
-
construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in a structured manner and must be willing and able to cooperate
-
will be developed using the OpenFOAM toolkit. A modelling workflow will be created, and then used as the basis for the optimisation, potentially using tools such as Bayesian Optimisation. In addition
-
optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
-
the overall structure of the cosmic web are the various versions of the scale-space MMF/Nexus pipeline and the stochastic Bayesian Bisous method. To improve, extend and deepen the analysis to a full dynamical
-
numbers of minimally-interpretable models being used, as opposed to traditional models like decision trees, or even Bayesian and statistical machine learning models. Explanations of models are also needed