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School of Engineering and Applied Science PhD Studentship (3 years) in Complex Adaptive Dynamics on Complex Networks

Aston University

Qualification Type: PhD
Location: Birmingham
Funding for: UK Students, EU Students
Funding amount: £14,777 per annum
Hours: Full Time
Placed On: 24th July 2018
Closes: 10th September 2018

Contract Type: Fixed Term
Closing Date: 10 September 2018
Supervisor: Dr Jens Christian Claussen

Start date: October, January, April or July. (Subject to negotiation).

Applications are invited for a three year full-time Postgraduate studentship, supported by the School of Engineering and Applied Science, to be undertaken within the System Analytics Research Institute and the Non-linearity and Complexity Research group at Aston University.   

Financial Support

This studentship includes a fee bursary to cover the home/EU fees rate, plus a maintenance allowance of £14,777 in 2018/19 (subject to eligibility).

Overseas Applicants

Applicants from outside the EU may apply for this studentship but will need to pay the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees, currently this is £12,290 in 2018/19.

Background to the Project

How does the complexity of the interaction topology contribute to the complexity of the dynamics in coupled adaptive systems? Major complex adaptive systems that have evolved in nature are: (i) the mammalian brain displaying function-specific topological structures albeit being composed of large numbers of mostly similar units, (ii) metabolic interaction networks that optimize processing of nutrients, and (iii) ecological networks formed by interacting species which adapt to the environment. In all three cases, the evolved interaction networks provide optimality and resilience often superior to simpler graph structures as regular lattices or random graphs.

The project intends to shed light on the functional contribution of the complexity of the network topology by comparing each one standard model from two of these classes of systems, along methodical lines:

  1. Utilizing network complexity measures to generate ensembles of networks of increasing complexity, and investigation how network complexity influences the complexity of the resulting network dynamics.
  2. Networks of Networks, Multilayer networks, and other hierarchical structures are currently considered for a refined description of complex systems, introducing more complexity in the interaction topology. In which systems does this result in improved resilience or other evolutionary benefits? Can this perspective provide insight towards improving processes in technology and society?
  3. Neural synapses adapt, axons grow, and alternative metabolic pathways fixate upon sufficient fitness advantage. Adaptation in biological and technological systems often coevolves by adaptation both in the units and the topology. In this project step, adaptation will be gradually shifted from local to topological adaptation, to support a general hypothesis that complex structures are beneficial for adaptive systems.

Person Specification

The successful applicant should be able to demonstrate strong research potential in application and interview, should have or expect to have a first class honours degree or equivalent qualification in Physics, Mathematics or a closely related discipline, and/or a Masters degree in a project related subject. As the project involves both analytic and numerical research, reasonable extent of both mathematical and programming skills are expected. Candidates that bring in project relevant expertise, e.g., computational systems biology, mathematical neuroscience, delay systems and control, stochastic processes, theoretical biology or statistical physics, are especially welcome.

Contact information

For informal enquiries about this project and other opportunities within the System Analytics Research Institute and the Non-linearity and Complexity Research group, contact Dr Jens Christian Claussen by email

For further information about the application process and details of how to apply, please visit the Aston University website here.

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