Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- ;
- ; University of Warwick
- Technical University of Denmark
- ; Newcastle University
- ; Swansea University
- ; University of Exeter
- ; University of Reading
- ; University of Southampton
- ; University of Sussex
- DAAD
- Forschungszentrum Jülich
- Institut Pasteur
- Ludwig-Maximilians-Universität München •
- Monash University
- NTNU - Norwegian University of Science and Technology
- Nature Careers
- Norwegian University of Life Sciences (NMBU)
- RMIT University
- Radboud University
- Technical University of Munich
- University of Bergen
- University of Groningen
- University of Oslo
- 13 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
-
for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
-
expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
-
sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
-
analyses, an area in which our group has a track record of success (see recent publications below). The TARGET-AI project seeks to apply leading-edge techniques from deep learning and Bayesian modeling
-
”, 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
-
infectious disease epidemiology and mathematical modelling in Biology and Medicine. Experience in parameter estimation, knowledge of Bayesian methods and computer programming skills would be an advantage. Good
-
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
-
(e.g. Bayesian Statistics, HMMs, AI, advanced programming in Python) in small classes of max. 10 participants. Lecture series: QMB students suggest, invite, and host external speakers at this event
-
behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore