Sort by
Refine Your Search
-
-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
-
-style interventions, and preventative medication. Analysis will utilise best practice in health inequalities measurement, modern econometric techniques, behavioural experiments, and modelling
-
known as Team COMPAS -- includes a number of amazing undergraduate and graduate students, postdocs, alumni, and other fantastic collaborators. Please contact me if you are interested in joining our group
-
Fellowship at LMU Munich, and a postdoc position at RMIT University. My nanophotonics research seeks to uncover the underlying physics in structured light-matter interactions at nanoscale. We aim to develop
-
comparing our experimental observations to predictions made using the Standard Model of Particle Physics. I am a member of the LHCb collaboration, one of the four large experiments at the Large Hadron
-
for dementia and sector-spanning models of care to improve quality of care and quality of life. Dr Ayton has a strong track record in health and social care research and methodological approaches including
-
My research interests focus on the stars - primarily their structure, evolution and nucleosynthesis. This can involve modelling of mixing in stars, or effects of changing nuclear burning rates
-
the development of numerical methods for astorphysical fluid dynamics and radiation transport. Projects may employ a range of approaches from analytic modelling and numerical calculations on desktop
-
I supervise a wide range of projects stellar astronomy. They include modelling stars in 1D or 3D, deciphering the origin of the elements (stellar nucleosynthesis), and observing using optical
-
Inference Tool (GAMBIT) Community I study various theoretical frameworks that extend the standard models of the elementary particles and cosmology to understand the nature of dark matter, dark forces and dark