Tagged: algorithm development, artificial intelligence, Bayesian inference, bifurcation theory, complex networks, computer science, data visualisation and analytics, epilepsy, machine learning, mathematics, neuroscience, nonautonomous dynamical systems
June 2, 2021 at 7:31 am #6852SMQB_UoBParticipant
The two successful candidates will join the Centre for Systems Modelling and Quantitative Biomedicine (SMQB), which brings together outstanding scientists from our Schools of Mathematics, and Computer Science, and the Institute for Metabolism and Systems Research.
The two roles are available due to the award of an EPSRC Established Career Fellowship to Professor John Terry, the SMQB Director. The purpose of the Fellowship is to develop new mathematical models and aligned computational tools to better understand how perturbations to dynamic brain networks impact upon seizure likelihood. A further objective is to enable translation of this understanding into more accurate, diagnosis, prognosis and management of epilepsy.
Summary of Role
There are two 3.5-year positions available to work with Professor John Terry on the Established Career Fellowship “Seizures and the brain: The role of perturbed dynamic networks”.
Position 1: To develop mathematical models that describe perturbations to brain networks due to internal (e.g. hormones, drugs, sleep) and external (e.g. stimulation or surgery) factors. Developed models will be calibrated using a variety of clinical data sets. Made available through an international network of collaborators, these data have been collected from a variety of sources including brain imaging, wearable technologies and smart-phone apps. Models should have both explanatory and predictive capacity and working with the wider team, they will be translated into prototype decision support systems enabling better diagnosis, prognosis and management for people with epilepsy.
Position 2: To develop computational methods that facilitate mathematical model calibration from clinical datasets. These data have been collected from a variety of sources including brain imaging, wearable technologies and smart-phone apps and made available through an international network of collaborators. By personalising models describing perturbations to brain networks, due to internal (e.g. hormones, drugs, sleep) and external (e.g. stimulation or surgery) factors, this will facilitate their translation. The ultimate aim being the design of prototype decision support systems enabling better diagnosis, prognosis and management of epilepsy.
The successful candidates will be educated to PhD level and have experience in one or more of the following areas:
Position 1 mathematical models; complex networks, (nonautonomous) dynamical systems, bifurcation theory or numerical continuation. Having relevant experience in biology or medicine will be advantageous.
Position 2 computational methods; machine learning, data visualisation and analytics, artificial intelligence, Bayesian inference, or algorithm development. Having relevant aligned experience in biology, medicine, or wearable technologies will be advantageous.
Curiosity led, with exceptional communication skills, they will thrive working in a highly interdisciplinary environment. They will be equally happy communicating with theoreticians, clinicians, industrialists and people with lived experience of epilepsy. Finally, they will possess a demonstrable ability to write up scientific findings for publication in a timely and succinct manner.
The successful candidate will join a dynamic research environment and receive strong mentoring and support in developing their own research interests, contributing to writing grants and generating research awards.
- You must be logged in to reply to this topic.