Research Associate

3 days ago

Edinburgh, United Kingdom University of Edinburgh Full time

**Grade 7: salary range: £35,333 - £42,155 per annum**

**College of Science and Engineering, School of Informatics**

**Fixed-term: Available for 2 years**

**Full time: 35 hours per week**

**The Opportunity**:
This project is funded by the UKRI New Horizons Scheme, aimed at supporting high-risk speculative engineering and ICT research. This inter-disciplinary project is aimed at developing new AI methods to advance the state of the art in materials synthesis by self-assembly of nanoparticles.

In this project, we envision three major innovations towards solving these problems. Firstly, we will develop new generative modelling approaches that could replace expensive ab-initio simulations of self-assembly across scales. Using the paradigm of Graph Neural Networks which have been successfully used to describe complex systems, we will develop new structured models that accurately capture the topology of structure-change dynamics in self-assembly, incorporating as inductive bias graph grammars, and multi-scale abstractions connecting levels of dynamics. Secondly, we will develop new methods for topological characterisation of the self-organising surface and use these in calibrating simulators to data, as well as to devise new algorithms for search and design optimisation. Finally, a major innovation will be in combining these to conduct optimisation not only in-silico, but directly and sample efficiently over runs of physical assembly through calibrated models and their use in topology-driven hence weakly controlled interventions to steer runs of the self-assembly process.

**Knowledge, skills and experience**

**Essential**:

- A PhD (or near completion) in AI, or an adjacent scientific area with strong computational and quantitative content.
- Background and practical experience with physics-informed machine learning, including Graph Neural Networks and related deep learning architectures.
- An excellent track record of publications in top tier conferences and/or journals in AI (e.g., AAAI, IJCAI, NeurIPS, ICML, ICLR) or corresponding venues in computational science.
- Excellent proven programming and systems engineering skills
- Ability to work effectively as part of a team, to meet deadlines, and to report on project progress.

**Desirable**:

- The PhD focussed on the use of machine learning to study complex physical phenomena, such as in materials science
- Understanding of geometric and topological methods, including computational topology and associated software tools
- Experience with numerical methods for simulation, such as molecular dynamics
- Exposure to materials synthesis, especially phenomena based on self-assembly and self-organisation.
- Demonstrated experience of the development and management of projects involving multiple stakeholders
- Ability to communicate complex information clearly, orally and in writing.
- Experience of research in the broad area of the related project
- Preferably, experience in the specific area of the related project
- Proven expertise in the techniques needed to undertake the research in question
- Demonstrated quality of research performance

Click here for a copy of the full job description

**As a valued member of our team you can expect**:
An exciting, positive, creative, challenging and rewarding place to work. We give you support, nurture your talent and reward success. You will benefit from a competitive reward package and a wide range of staff benefits, which includes a generous holiday entitlement, a defined benefits **pension scheme **, **staff discounts, **family friendly initiatives **, flexible working and much more. Access our **staff benefits page **for further information and use our **reward calculator **to find out the total value of pay and benefits provided.

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.

If invited for interview you will be required to evidence your right to work in the UK. Further information is available on our **right to work **webpages

As a world-leading research-intensive University, we are here to address tomorrow’s greatest challenges. Between now and 2030 we will do that with a values-led approach to teaching, research and innovation, and through the strength of our relationships, both locally and globally.

Informatics is the study of how natural and artificial systems store, process and communicate information. Research in Informatics promises to take information technology to a new level, and to place information at the heart of 21st century science, technology and society. The School enjoys collaborations across many disciplines in the University, spanning all three College, and also participates as a strategic partner in the Alan Turing Institute and, with Heriot-Watt University, i

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