Research projects

Managing, analysing and interpreting large, complex datasets and high rates of data flow is a growing challenge for many areas of science and industry. Recent years have witnessed a dramatic increase of data in many fields of science and engineering, due to the advancement of sensors, mobile devices, biotechnology, digital communication, and internet applications.

LIV.DAT Centre for Doctoral Training (CDT) provides a comprehensive training programme for PhD students in data intensive science to address this problem. The focus of the centre will be on addressing the data challenges presented by research in astronomy, nuclear, particle and accelerator physics. 

R&D is structured across three main Work Packages (WP):

Monte Carlo and Model Definition (WP1)

Monte Carlo and Model Definition (WP1)

Monte Carlo (MC) methods are powerful tools for everything from modelling the birth and evolution of the universe to performing calculations. Explore our projects in this area.

Deep Learning and HPC (WP2)

Deep Learning and HPC (WP2)

High Performance Computing (HPC) and Deep/machine Learning is the research focus of this work package. Read more about our projects around Deep Learning and HPC.

Data Analysis (WP3)

Data Analysis (WP3)

Our approach in this area involved Bayesian inference techniques that complement Deep Learning, explore the projects we are working on in this area.