Topic: Data-driven control

The Chair in Nonlinear Dynamics recruits a doctoral researcher to develop interpretable, data-driven models for complex physical systems. Work centres on spectral submanifold methods and nonlinear model reduction, with possible applications to fluid–structure interaction, materials with memory, soft robotics, teleoperation, space structures and turbulence. The doctorate can be based in mechanical engineering or mathematics according to the candidate’s background. The start is flexible, with an early start preferred. Apply online with a CV, bachelor’s and master’s transcripts and at least two r

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers.