Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Chalmers University of Technology
- Inria, the French national research institute for the digital sciences
- Ludwig-Maximilians-Universität München •
- Norwegian University of Life Sciences (NMBU)
- Sveriges Lantbruksuniversitet
- Swedish University of Agricultural Sciences
- University of Oslo
- Uppsala universitet
-
Field
-
. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification
-
chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
-
powered by: Cookie Information Nettsiden bruker cookies Vi ønsker at du skal være trygg når du bruker dette nettstedet. Vi benytter cookies for å sikre at du får en best mulig brukeropplevelse og
-
, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
-
Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
approach is based on neural techniques known as SBI (Simulation-Based Inference) [Cranmer et al., 2020]. SBI enables the resolution of inverse problems using generative AI methods and Bayesian statistics
-
, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
-
, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
-
project-specific requirements. Students can deepen their knowledge about selected topics (e.g. Bayesian Statistics, HMMs, AI, advanced programming in Python) in small classes of max. 10 participants