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Active anomalous stochastic search

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing either in September 2020 for students seeking funding, or in January 2020 or April 2020 for self-funded students. The deadline for funded applications is the 31st of January 2020. The deadline for China Scholarship Council Scheme applications is 12th January 2020.

This project will be supervised by Dr. Rainer Klages.

Background: Biological dynamics are characterised by activity in the sense that energy is taken up from the environment and converted into motion [1]. Examples of self-propelled motion range from human movement, to foraging marine predators, to crawling biological cells. Active dynamics are very different from passive Brownian motion, where a tracer particle is driven by collisions with the surrounding
fluid particles. Biological processes furthermore often exhibit anomalous dynamics characterised by long-term diffusion that is, again, very different from Brownian motion [2]. This reflects the spatio-temporal complexity of biological systems. Mathematically both active and anomalous dynamics are modeled in terms of advanced (persistent, non-Markovian, non-Gaussian) stochastic processes [1,2]. This project will cross-link these two very recent, new fields of research for understanding the search of targets like, e.g., food sources in a foraging process [3].

Project description: The warm-up will be to learn about Levy walks, a famous fundamental class of anomalous stochastic processes [4]. Their diffusive properties should be understood analytically and explored numerically in simple search scenarios, formulated mathematically as first passage and first arrival problems. While much is known about these dynamics in one dimension, higher dimensional settings are at the forefront of research. This knowledge should be applied to understand new experimental
data by G.Volpe (London) and V.Trianni (Rome) on biological and robotic Levy walkers. The impact of biological activity and interactions between single particles on search efficiency should be explored by developing and analysing new stochastic models.

The application procedure is described on the School website. For further inquiries please contact Dr Rainer Klages at . This project is eligible for full funding, including support for 3.5 years’ study, additional funds for conference and research visits and funding for relevant IT needs. Applicants interested in the full funding will have to participate in a highly competitive selection process.

Funding Notes

This project is eligible for full funding, including support for 3.5 years’ study, additional funds for conference and research visits and funding for relevant IT needs.

This project can be undertaken as a self-funded project. Self-funded applications are accepted year-round for a January, April or September start.

We welcome applicants through the China Scholarship Council Scheme (deadline for applications 12th January 2020).

The School of Mathematical Sciences is committed to the equality of opportunities and to advancing women’s careers. As holders of a Bronze Athena SWAN award we offer family friendly benefits and support part-time study.

References

C.Bechinger, R.Di Leonardo, H.L¨owen, C.Reichhardt G.Volpe, G.Volpe, Rev.Mod.Phys. 88, 045006 (2016)

R.Klages, G.Radons, I.M.Sokolov (Eds.), Anomalous Transport: Foundations and Applications (Wiley-VCH, Berlin, 2008)

R.Klages, Search for food of birds, fish and insects, book chapter in: A.Bunde, J.Caro, J.Kaerger, G.Vogl (Eds.), Diffusive Spreading in Nature, Technology and Society. (Springer, Berlin, 2017)

V. Zaburdaev, S. Denisov, J. Klafter, Rev.Mod.Phys. 87, 483 (2015)

How good is research at Queen Mary University of London in Mathematical Sciences?

FTE Category A staff submitted: 34.80

Research output data provided by the Research Excellence Framework (REF)

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