information engineering approach.
We are excited to announce an EPSRC-funded PhD studentship opportunity in the field of computational neuroscience, in the Department of Engineering, University of Cambridge.
Aims. This PhD project uses an information engineering approach to unravel the dynamic neural mechanisms orchestrating the maintenance of body homeostasis and endogenous pain regulation. Endogenous pain regulation refers to the intrinsic ability of the central nervous system to modulate the intensity and perception of pain. Unlike exogenous interventions such as medications, endogenous regulation involves the body's internal mechanisms for controlling and mitigating harm. This intricate process encompasses a network of neural circuits and neurotransmitter systems that dynamically interact to either enhance or suppress the transmission of bodily signals.
Methods. The project involves developing and using computational methods to analyse neural dynamics in the brain and spinal cord involved in homeostasis and endogenous regulation of pain. These involve the use of large-scale simulations of neural activity using neural network models. The incorporation of control theory principles enhances the study by providing a systematic framework for analyzing the regulatory processes within the neural network. Control theory, commonly applied in engineering contexts, allows for the modeling and understanding of feedback loops and adaptive responses within complex systems¿attributes highly relevant to the dynamic nature of endogenous pain regulation. There will also be the opportunity to work with high- resolution neuroimaging data, if desired.
Expected outcomes. This multidimensional investigation not only seeks to advance our understanding of endogenous pain regulation but also holds promise for the development of targeted interventions for chronic pain conditions. By elucidating the neural substrates and dynamic patterns governing pain modulation, this research may contribute to the refinement of therapeutic strategies and the advancement of personalized pain management approaches.
Candidate profile. With, or expected to gain a high 2:1, preferably a 1st class honours degree in Engineering, Neuroscience, Computer Science, Physics/Mathematics or related discipline.
Environment. The Computational and Biological Learning group uses engineering approaches to understand the brain and to develop artificial learning systems. Research includes Bayesian learning, computational neuroscience, statistical machine learning and sensorimotor control. The work on machine learning includes both theory and applications to vision, information retrieval and bioinformatics. The work on human learning includes both computational modelling and experimental approaches using robotic and virtual reality interfaces. More info: https: // www. cbl-cambridge.org/
https: // www. noxlab.org/
This PhD opportunity is fully-funded (fees and maintenance) for eligible UK students. EU and international students may be considered for a small number of awards at the UK rate. Full eligibility criteria can be found via the following link: https: // www. postgraduate.study.cam.ac.uk/finance/fees/what-my- fee-status
To apply for this studentship, please send your two page CV and statement of research interests to Dr Mancini [email protected] to arrive no later than 17/03/2024. Applications may close early if the position is filled before this date.
Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately for admission through the University's Graduate Admissions application portal; this can be done before or after applying for this funding opportunity. The applicant portal can be accessed via: www. graduate.study.cam.ac.uk/courses/directory/egegpdpeg. The final deadline for PhD applications is 16 May 2024, although it is advisable to apply earlier than this.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
Department/LocationDepartment of Engineering, Cambridge
ReferenceNM40296
CategoryStudentships
Published26 January 2024
Closing date17 March 2024