Subject areas: Deep learning, soft matter, active matter, targeted delivery, self-assembly, biosensing, computational sciences.

Supervisors: Dr Giorgio Volpe (UCL), Dr Yuan Cheng (IHPC, A*STAR, Singapore), and Dr Ran Ni (NTU, Singapore)

Application deadline: Applications will be accepted until 31 July 2021 but the position will be filled as soon as an appropriate candidate is found.

Start Date: 27 September 2021

Location: London (1.5 years), Singapore (2 years)

The Studentship

This position is fully funded by the UCL-A*STAR Collaborative Programme via the Centre for Doctoral Training in Molecular Modelling and Materials Science (M3S CDT) at UCL. The student will be registered for a PhD at UCL where he/she will spend year 1 and the first six months of year 4. The second and third years of the PhD will be spent at the A*STAR Institute of High Performance Computing in Singapore. The studentship will cover tuition fees at the Home rate, and an annual stipend of no less than £17,285 increasingly annually with inflation (tax free) pro rata in years 1 and 4. During years 2 and 3, the student will receive a full stipend directly from A*STAR. In addition, A*STAR will provide the student with one-off relocation allowance.

The Project

For over a decade now, the soft matter research community has been fascinated with creating small particles that are capable of autonomous motion at the microscopic and nanoscopic scale. The drive for this is the hope to engineer micro- and nanocarriers that can perform complex operations and tasks at the microscale. One of the most promising tasks is the possibility to realize tiny biocompatible agents that can navigate the human body autonomously to deliver cargoes, such as drugs or genetic material, in a targeted way, thus significantly improving on state-of-the art delivery and biosensing technologies. Despite extensive research, including recent advances from our group, this task still remains elusive to date. The aim of this collaborative project is to develop an efficient computational framework for the in-silico design of self-propelling microscopic particles that can autonomously navigate towards a biological target and bind to receptors expressed on it relying on selective and reversible DNA-based chemical binding schemes. In order to achieve this goal, we will use different numerical techniques (e.g. Brownian dynamics and deep-learning approaches with neural networks) to realize efficient materials modeling and design

The Candidate

The successful applicant should have or expect to achieve at least a 2.1 honours or equivalent for undergraduate degree in Computational Chemistry, Physics, Materials Science, Engineering or a related discipline. The successful applicant will demonstrate strong interest and self-motivation in the subject, excellent programming skills (in C++, Matlab, Python or equivalent) and the ability to think analytically and creatively. Good computer skills, plus good presentation and writing skills in English, are required. Previous research experience in contributing to a collaborative interdisciplinary research environment is highly desirable but not necessary as training will be provided.

Please contact Dr Giorgio Volpe ( or Dr Yuan Cheng (  or Dr Ran Ni ( for further details or to express an interest.

Funding Notes

Due to funding restrictions, this studentship only opens to UK nationals, EU nationals with settled/pre-settled status. Please note that we are currently seeking clarity from the Department for Education on how EU students with pre-settled and settled status will be considered in terms of fee status as the studentship only covers home fees.

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