The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence. The Institute is named in honour of the scientist Alan Turing and its mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.

This project will be run within the UKRI-funded programme on AI for Science and Government, based at The Alan Turing Institute. These programmes are focused on research in data science, with accompanying translational activities to ensure impact in the fields of applied science, engineering, urban analytics governance, in keeping with the vision, mission and charitable aims of the Turing Institute. The role will also be associated with the Lloyds’ Registry Foundation funded programme on Data-Centric Engineeering within the Alan Turing Institute.

This post is an appointment to the Digital Twins for Fleets and Supply Chains project within the programme on AI for Science and Government at the Alan Turing Institute. You will join a team of researchers affiliated with the Alan Turing Institute led by Prof Mark Girolami (Cambridge, Engineering) and Dr Andrew Duncan (Imperial, Statistics) and working on projects related to the development of methodology for the integration of system-level digital twins. The applicant will be expected to manage collaborations with other researchers within the ASG programme, as well as with the Data Centric Engineering Programme at the Turing and engage with relevant stakeholders from government bodies and industry.

BACKGROUND
We are seeking to recruit two postdoctoral research associates to work on developing theory, methods and tools to enable the development of systems-level digital twins to enable effective monitoring, planning and scenario-testing for fleets (transport or otherwise) and supply-chains.

The project builds on ongoing research within the ASG programme for complex systems engineering which has been developing methodology for the development, calibration and deployment of digital twins, with specific applications in built and urban infrastructure, multiphase flow systems and aero-engine manufacturing.

This project will focus on methodological challenges involved in linking together heterogeneous collections of digital twins to derive insight at the collective level. Research questions relating to efficient calibration, propagation of uncertainty, decision making under uncertainty and planning will be considered, specific to the application area under consideration. The candidate will be expected to collaborate with other teams within this programme who aim to leverage these developments for specific case-studies across multiple sectors.

You will be expected to perform high quality research under the supervision of the principal investigators. Specifically, you will produce breakthrough research in this nascent field of research and contribute to publishing these results in top rated journals and at national and international conferences, as appropriate.

You will possess a PhD in Statistics, Mathematics, Engineering, Computer Science, or related discipline. You should have a strong background in one or more of the following areas: Bayesian Inference and probabilistic machine learning, uncertainty quantification and data assimilation methods for online calibration of time-dependent models, mathematical modelling of collective behaviour of multi-agent systems, planning and decision making under uncertainty.

Informal enquiries may be addressed to Prof Mark Girolami (mag92@eng.cam.ac.uk) . Please note that applications sent directly to this email address will not be accepted.

DUTIES AND RESPONSIBILITES

• To establish a sound research base within the Alan Turing Institute in order to pursue individual and collaborative research of outstanding quality, consistent with making a full active research contribution in line with the research strategy outlined by the PIs.
• To write or contribute to publications or disseminate research findings using other appropriate media.
• To attend and present research findings and papers at academic and professional conferences, and to contribute to the external visibility of the Institute.
• To ensure compliance with secure handling of data and health and safety in all aspects of work.
• To participate in and develop internal and external partnerships, for example to identify sources of funding, generate income, obtain projects, or build relationships for future activities.
• Teaching might be required as part of collaboration work.

Essential

• Research Associate level: PhD in Mathematics, Statistics, Engineering, Computer Science or closely related discipline.
• Research Assistant level: Near completion of a PhD or equivalent level of professional qualification in Mathematics, Statistics, Engineering, Computer Science or closely related discipline.
• A solid background in one or more of the following: Bayesian inference and robabilistic machine learning, uncertainty quantification and data assimilation Methods for online calibration of time-dependent models, planning and decision making under uncertainty.
• Track record of the ability to initiate, develop and deliver high quality research aligned with the research strategy indicated by the PI and any industrial stakeholders and to publish in peer reviewed journals and conferences.
• Teaching may be required as part of the role.
• Commitment to meeting deadlines.
• Flexible attitude towards work.
• Commitment to EDI principles and to the Organisation values.

Desirable

• Experience in design, development and implementation of research software libraries, ideally using one of the following: Python, R, Julia and their associated frameworks.
• The ability to initiate, plan, organise, implement and deliver programmes of work to tight deadlines.
• Good effective communication (oral and written) skills, presentation and training skills.
• Good interpersonal skills.
• A developing track record in producing high quality academic publications.
• Ability to write research reports and papers in styles accessible to both academic and lay audiences.
• The ability to work in a team and interact professionally within a team of researchers and PhD students.

Other Requirements

• Commitment to meeting deadlines
• Flexible attitude towards work
• Commitment to EDI principles and to the Organisation values

APPLICATION PROCEDURE
If you are interested in this opportunity, please click the apply button below. You will need to register on the applicant portal and complete the application form including your CV, covering letter and contact details for your referees. If you have questions about the role or would like to apply using a different format, please contact them on 0203 862 3357 or 0203 862 3340, or email recruitment@turing.ac.uk.

CLOSING DATE FOR APPLICATIONS: 13 December 2020 at 23:59​.

TERMS AND CONDITIONS
This full-time post is offered on a 2 year fixed-term basis starting 1 January 2021. The annual salary is £35,000-£41,000 (depending on experience) plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits.

Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £32,000-£34,000 per annum.

This job description is written at a specific time and is subject to change as the demands of the Institute and the role develop. The role requires flexibility and adaptability and the post holder needs to be aware that they may be asked to perform tasks and be given responsibilities not detailed in this job description.

EQUALITY, DIVERSITY AND INCLUSION
The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly. In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital or civil partnership status, pregnancy and maternity, religion or belief, sex and sexual orientation.

Please note all offers of employment are subject to continuous eligibility to work in the UK and satisfactory pre-employment security screening which includes a DBS Check.

Full details on the pre-employment screening process can be requested from HR@turing.ac.uk.