Subject areas: Deep learning, soft matter, active matter, photonics, self-assembly, 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 20 July 2020 but the position will be filled as soon as an appropriate candidate is found.
Start Date: 28 September 2020
Location: London (1.5 years), Singapore (2 years)
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 UK/EU rate plus a maintenance stipend about £17000 (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. Please note that, due to funding restrictions, only UK/EU citizens are eligible for this studentship.
Hyperuniform materials are a novel class of disordered materials with properties of both liquids and solids. Because of this dual nature, such materials are increasingly interesting for photonics as they can influence the path of light with the efficiency of a crystal while retaining the flexibility of a liquid. The aim of this collaborative project is to develop an efficient computational framework to design optimal hyperuniform materials whose structure can be rearranged dynamically into different configurations capable of unique interaction with light. In particular, this project will develop a new efficient numerical scheme based on the power of deep-learning approaches with neural networks to realize efficient modeling and design of active hyperuniform materials. If properly designed, this level of tunability is promising to realize novel robust functional materials for photonics and beyond.
The successful applicant should have or expect to achieve at least a 2.1 honours or equivalent for undergraduate degree in 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 (firstname.lastname@example.org) or Dr Yuan Cheng (email@example.com) or Dr Ran Ni (firstname.lastname@example.org) for further details or to express an interest.