Discovery of structure and dynamical laws


Qualification type: PhD

Location: Hatfield, United Kingdom

Funding for: UK and EU applicants

Funding amount: Tax free bursary (currently £15,285 per annum for the academic year 2020-21).

Closing application date: 11 January 2021

Project outline

A PhD studentship is available at the University of Hertfordshire under the supervision of Daniel Polani for the discovery of structure and dynamical laws; the candidate will use information theory to extract structural properties of both observed data and data from actively manipulated environments.

Applying for above PhD studentship, you will be interested in principled AI approaches, with special focus on information-theoretical methods, to extract and identify structural and dynamical laws of given systems, both under passive observation as well as active manipulation. Possible fields of applications will include topics such as principled and systematic approaches towards model construction by agents, robotics, intelligent methods for system identification and reduction and similar.

Your work will be embedded in the research of the SEPIA (Sensor Evolution, Processing, Information and Actuation) group which has a particular specialisation on developing methods dealing with fundamental issues of Artificial Intelligence, and then applying them in various contexts, such as in robotics, biological modelling, games and other. The SEPIA group is part of the encompassing Adaptive Systems Research Group which includes a highly interdisciplinary team of researchers and is itself part of the School of Physics, Engineering and Computer Science at the University of Hertfordshire, UK.

There will also be the opportunity to collaborate with the School’s successful humanoid robot RoboCup team, the Bold Hearts, and a wide range of additional projects.

Essential criteria

You will have an excellent first degree and a very keen interest and motivation in delving into highly innovative, challenging and topical area of AI. An excellent background in one of the following or a related field is essential: computer science, computational/cognitive robotics, physics, mathematics, statistics or any other relevant discipline with a considerable quantitative/computational component. Prior experience with topics such as Bayesian modelling, information theory, control theory, dynamical systems theory, Lie algebras, computational topology, statistical learning or similar fields is highly desirable, but not essential if the quantitative background is otherwise very strong. Excellent programming skills in at least one major computer language are essential.

Application process

Applicants are required to provide the following documents:

  • A completed application form (PDF - 0.25 Mb)
  • A research statement stating past experience/research background and interest and motivation/research ambition for current project
  • Two academic references
  • Copies of qualification certificates and transcripts
  • A copy of your passport

Please send completed applications to the Doctoral College at

For enquiries concerning this studentship please email Professor Dr Daniel Polani.