Research Fellow

University of Aberdeen - School of Natural and Computing Sciences

We are seeking a Research Fellow to contribute to the three-year EPSRC-funded project Manufacturing Immortality, working in the areas of machine learning, data mining, and systems modelling in facilitating the design and manufacturing self-healing bio-hybrid products.

Manufacturing Immortality is a >£2.7M EPSRC-funded research project focused on the development of evolvable self-healing products and devices. This project brings together seven UK universities (Aberdeen, Bristol, Canfield, Lancaster, Manchester, Northumbria and Sheffield Hallam), along with a number of industrial partners and allied stakeholders. The aim of this project is to develop evolvable bio-hybrid systems (materials, devices, etc.) that possess the inherent capacity to autonomously sense and repair damage. The evolvable self-healing products and devices will be used in three application areas: electrochemical energy devices, e.g. fuel cells and batteries; consumer electronics; and safety critical systems, e.g. nuclear and deep sea technologies.

The successful candidate will join Dr Wei Pang’s research team in the department of computing science and work as part of a collaborative multidisciplinary team progressing a research project at the interface of the physical and life sciences. The main focuses of this role are (1) to develop novel machine learning approaches to guide the integration of biological and materials systems across all scales, from atomistic to macroscopic, (2) to build predictive models for directing product and device assembly, (3) multi-scale modeling and simulation to assess the quality of the assembled bio-hybrid systems, and (4) data mining to interpret data from prototype products for better understanding the material and its potential commercialization.

The successful candidate will have a good knowledge of machine learning, data mining, and complex systems modelling. Experience of predictive modelling, data clustering, support vector machines, deep learning, and dynamic system simulation is desirable. In addition, the candidate should be able to effectively communicate with other team members from all seven universities and industrial partners. You will have a PhD or be about to complete a PhD in computer science, data science or a cognate discipline. Enthusiasm for data analytics and complex system modelling in real-world problem solving, especially biological, materials, or manufacturing problems is essential, as is the ability to work in a multidisciplinary team.

This post is externally funded by EPSRC and is available for a period of 36 months. Salary will be at the appropriate point on the Grade 6 scale (£32,548 per annum), with placement according to qualifications and experience. Consideration will be given to making an appointment at Research Assistant, Grade 5 level in the first instance (£27,285- £30,688 per annum) for individuals in the final stages of completing their PhD.

Informal enquiries should be made to Dr Wei Pang, e-mail:  pang.wei@abdn.ac.uk

Should you require a visa to undertake paid employment in the UK you will be required to fulfil the minimum points criteria to be granted a Certificate of Sponsorship and Tier 2 visa. As appropriate, at the time an offer of appointment is made you will be asked to demonstrate that you fulfil the criteria in respect of financial maintenance and competency in English. Please do not hesitate to contact Marian Elliott-Jones, m.elliott-jones@abdn.ac.uk for further information.

To apply online for this position visit www.abdn.ac.uk/jobs

The closing date for the receipt of applications is 22 January 2018

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