PhD - Stochastic Dynamics and Thermodynamics of Living Systems

About the Project

The project

Life at the microscale is noisy, inevitably impacted by thermal fluctuations. On the one hand, noise plays a negative role, hampering the ability to perform tasks reliably. On the other hand, systems that are suitably driven out of equilibrium can exploit fluctuations to achieve transport, efficient energy conversion, and build biochemical circuits and switches. How do living systems deal with fluctuations without being overwhelmed by them? How do cells employ their stochastic machinery to process noisy signals and nonetheless perform tasks robustly? How much does this cost them? Is it possible to exploit fluctuations to design efficient microscopic engines? The PhD project will address these general questions from the theory viewpoint by focusing on specific biological systems such as biomolecular condensates, molecular motors, etc…, within ongoing experimental collaborations. We aim to understand how fluctuations originating at the molecular scale propagate to larger scales, how they are affected by larger-scale processes, and, ultimately, how they give rise to macroscopic consequences. The prospective student will investigate these topics within the framework of non-equilibrium thermodynamics of small systems, making use of, and developing tools from stochastic processes, stochastic optimal control, information theory, and machine learning. Concerning machine learning, special attention will be devoted to the interpretability of the methods.

The candidate

Applicants should have, or expect to have, an integrated Master’s (e.g., MSci) with first-class honours or upper division second-class honours (2:1), or a BSc plus Master’s (MSc) degree with Merit or Distinction in Physics, Biophysics, Applied Mathematics, or related subject. The successful applicant will demonstrate strong interest and motivation in the subject, and ability to think critically and creatively. Previous research experience in theoretical physics and or an interdisciplinary research environment is desirable.

Interested candidates should initially contact the supervisor (Stefano Bo, stefabo@pks.mpg.de) with a transcript, CV, and motivation letter expressing interest in the project. Informal enquiries are encouraged.

All shortlisted applicants will be invited to interview no more than 4 weeks after the application deadline. Applications will be accepted until 31st March 2023, though applications will be assessed and interviewed on a rolling basis.

Funding Notes

This position is fully funded for 3.5 years by the Faculty for Natural, Mathematical, and Engineering Sciences at King’s. Funding covers:
• A tax-free stipend of no less than £19,668
• UK tuition fees
• A 'Research Training Support Grant' to cover research expenses
Exceptional international students may be eligible to apply for a competitive fee waiver for the difference between UK-level and international-level tuition fees. This option will be discussed with shortlisted candidates.

References

1. Bo S., Hubatsch L., Bauermann J., Weber C.A., and Jülicher F. “Stochastic dynamics of single molecules across phase boundaries.” Phys. Rev. Res. 3, 043150 (2021). See also Physics 14, s152.
2. Argun A., Volpe G. and Bo S. “Classification, inference and segmentation of anomalous diffusion with recurrent neural networks” J. Phys. A 54 294003 (2021).
3. Bo S., Schmidt F., Eichhorn R. and Volpe G. “Measurement of anomalous diffusion using Recurrent Neural Networks”. Phys. Rev. E 100 (1), 010102 (2019).
4. Bo S. and Eichhorn R., “Driven diffusion at boundaries: noise rectification and particle sorting”, Phys. Rev. Lett., 119 (6), 060603 (2017).