Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
-
Field
-
, the project takes advantage of the unique long-term datasets collected in Finland. REC also develops state-of-the-art methodology for analysing long-term spatially structured data sets within a joint species
-
structural deviations by leveraging lifespan normative models for cortical gyrification and grey-matter volume, and (iii) characterise and predict longitudinal brain change in psychosis by estimating
-
projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
-
experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
-
models, analysing structural properties, and developing innovative algorithms with both theoretical rigor and practical relevance. Where to apply Website https://www.academictransfer.com/en/jobs/354359
-
of the project. Apply established techniques and develop new methods inspired by Bayesian methods and statistical physics methodology for understanding the emergence of structure within cortical organoids and
-
-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
-
surveillance) sensors can also be seen as temporal events. While data from current sensors can be manually converted into events for fast processing, it is also possible to develop hybrid structures where some
-
related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals