Sort by
Refine Your Search
-
will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
-
; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
-
techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference
-
the weblike distribution of galaxies in large galaxy redshift surveys, with the corresponding velocity flows inferred from measured galaxy peculiar velocities. These measurements are to be converted into a
-
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
-
the corresponding velocity flows inferred from measured galaxy peculiar velocities. These measurements are to be converted into a multiscale analysis of the mass distribution, as well as that of the flow field
-
Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
-
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