Sort by
Refine Your Search
-
Your Job: Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure
-
Midlands Graduate School Doctoral Training Partnership | Loughborough, England | United Kingdom | 3 months ago
of Nottingham to commence in October 2026. Project overview This project investigates how housing inequalities spatially structure wellbeing in cities. It begins from the premise that where we live — and the
-
to domain-specific knowledge and research. All three domains – life & medical sciences, earth sciences, and energy systems/materials – are characterized by the generation of huge heterogeneously structured
-
heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
-
explicitly extend these models to capture temporal structure within spike trains thereby moving towards analyses that are sensitive not just to firing rates but also precise timing relationships underpinning
-
algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
-
population-level neural interactions. Prior work has emphasized rate-based codes due to their relative simplicity; our approach will explicitly extend these models to capture temporal structure within spike
-
geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
-
complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical approaches, the application of meta learning, and the integration of convex
-
environment at the interface between neuroscience and digital technologies, enabling scientific progress on the most complex known systems Outstanding scientific and technical infrastructure A highly motivated