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University and departmental processing times can be long and we highly recommend students to submit their applications by the end of November 2018, in order to ensure that they receive full consideration by the TCM group for admission in the academic year 2019/20. Further details on the application and selection process are provided below.
The Theory of Condensed Matter group usually accepts a little over half a dozen PhD students each year.
Research areas include Electronic Structure Computation, Soft Condensed Matter, Statistical Physics, Collective Phenomena, Strongly Correlated Systems and Biophysics. There is no rigid list of projects. Prospective applicants are strongly encouraged to contact potential supervisors whose research is of interest. Even if you do not receive a reply, such an approach is often a useful first step.
Those potentially interested in applicants for the next academic year are: Ahnert, Artacho, Beri, Castelnovo, Collepardo, Conduit, Cooper, Lamacraft, Lee, Needs, Nunnenkamp, Payne, Teichmann, and Warner. However, please note that any Principal Investigator in the group can supervise postgraduate students.
Examples of PhD projects for students admitted in the academic year 2019-20
Dr Collepardo We develop new multiscale computer simulation approaches, exploiting principles from physics, chemistry, computer science, and biology, to unravel the physical mechanisms explaining the connection between genome characteristics, organization and function. We are offering two ERC-funded PhD studentships for October 2019 to work on the projects: (1) Physical determinants of genome nanostructure and (2) Liquid-liquid phase separation in the genome.
Dr Monserrat: I am interested in developing quantum-mechanical methods to predict the properties of materials in (1) dissipationless electronics and spintronics: Chern insulators and topological insulators, (2) solar cells: the optoelectronic response of photovoltaic materials, and (3) high-pressure astrophysics: the behaviour of matter under extreme pressures such as those in planetary interiors.
Prof. Needs: The structures of some materials are stabilised by anharmonic nuclear motion. If the vibrations in a structure stabilised in this way were to be magically `switched off' it would relax to a quite different structure. Such behaviour can only occur when the vibrations are strongly coupled to one another. Structures stabilised by anharmonic vibrations may be close to instabilities which can lead, for example, to properties such as high-Tc superconductivity. The world record superconducting material H3S with a transition temperature of 200 K shows such behaviour. The aim of this project is to discover new structures that are stabilised by anharmonic motion and to determine whether they show interesting and unusual properties.
Dr Nunnenkamp: Quantum coherence in atomic, mesoscopic, and optical systems, in particular cold atoms, superconducting circuits, and cavity optomechanics. Current interests include dissipative phase transitions, e.g. quantum synchronization and cold atoms in optical cavities, topological phases of light and sound, as well as hybrid systems for quantum technology.
Prof. Cooper: Topological phases of matter and far-from-equilibrium dynamics of many body quantum systems. Projects are inspired by recent experimental developments in novel electronic systems and in cold atomic gases. Current interests include the role in topology in far-from-equilibrium quantum systems; strongly coupled Majorana fermions; excitonic insulators out of equilibrium.
Dr Castelnovo: Decoherence and dephasing in (frustrated) magnetic systems. I am interested in understanding how quantum effects arise as thermal excitations become weaker and weaker; and how a system transitions from classical to quantum spin liquid behaviour.
Dr Conduit: proposes two projects, one in strongly correlated systems, and the second in electronic structure:
- The standard picture of superconductors is that the opposite spin particles attract and pair. However, in certain systems the "Cooper pair" could in fact be made from three or more particles. Using a combination of numerics and analytics we will explore the energetics and the properties of such a superconductor.
- New materials are essential to enable new technologies, but are currently discovered in an experimental driven process of trial and improvement that can take up to 20 years. This project will focus on the design of new materials using an innovative approach based on artificial intelligence. We merge experimental data and quantum mechanical computer calculations to take advantage of everything that is known about the material. The project will be in colaboration with BP scientists and will be run within the Centre for Doctoral Training in Computational Methods for Materials Science.
Dr Teichmann: I would like to offer PhD projects in the area of statistical physics models and machine learning applied to large biological data sets, including single cell genomics data. Some of this work will be done in collaboration with Ben Simons.
Dr Beri: Novel phases of matter emerging from topology, interactions and symmetries. Possible PhD project areas include developing schemes for creating and detecting such phases in quantum many-particle systems, as well as exploring quantum information related features from fundamental and application perspectives.
Dr Ahnert: Research areas include: genotype-phenotype maps of protein structure, biological evolution of structural and functional complexity, interdisciplinary applications of network analysis.
Dr Lee: Combining machine learning with physics to discover soft materials. We aim to bridge the gap between organic molecules and functional materials. Current interests include using unsupervised learning to extract new physical principles from high throughput simulations and experiments, designing organic electrolytes for batteries and supercapacitors, computational drug discovery, and predicting the outcome of organic reactions.
Dr Lamacraft: The two broad areas of research are:
- Dynamics of quantum information in random or noisy systems see e.g. this recent paper.
- Machine learning methods in physics in particular generative models for modelling statistical ensembles or dynamical systems.
The second project would be largely numerical. The first one would have both an analytical and numerical component.
The application process
You must apply for graduate admission to the Physics Department. Relevant applications reach TCM for further selection, shortlisting, and interviews.
In your application, you should specify that you are interested in projects in TCM. You should also include the names of all potential supervisors that you are willing to consider working with. Ideally, these should be ranked in order of preference (the names and the ordering can later be altered at or shortly after the interview, if invited). Above in this same page you find a list of supervisors who are likely to look for postgraduate students this coming academic year.
There is no strict application deadline but in order to receive full consideration in the TCM group, we strongly encourage students to submit their applications by the end of November of the year preceding the start of the PhD. TCM aims to hold at least two rounds of interviews, typically in late January and in late February.
Shortly after the interview, the applicants will be asked to provide a final ranked list of the supervisors they are willing to work with in TCM, which will form the basis for the final selection process before official offers are made.
TCM will continue to consider applications until all available positions have been filled. If you are unsure whether we are still accepting applications, please contact the TCM group administrator.
Your eligibility for funding from different sources will depend on your individual circumstances. Details can be found on the departmental website. Some of these sources have their own internal deadlines and you should make sure that your application is submitted in time.
TCM interviews of postgraduate applicants (by invitation only) are either conducted in person (where possible) or remotely (phone, Skype), by a panel. Interviews typically begin with a 10 minute presentation by the candidate on a project or extended piece of work they have recently completed, or on a research topic they are familiar with. This is followed by approximately 20 minutes of questions, not limited to the topic of the presentation.
Centre for Doctoral Training
Applicants interested in the computational materials modelling work of the group should also consult the (separate) admission procedure of the Centre for Doctoral Training in Computational Methods for Materials Science (the two applications are not mutually exclusive).