CRANFIELD UNIVERSITY

Research Fellow in Reinforcement Learning for Engineering

Location
Cranfield, United Kingdom
Salary
£33,309 to £37,127 per annum
Posted
22 Jun 2020
End of advertisement period
30 Jul 2020
Ref
3358
Contract Type
Fixed Term
Hours
Full Time

School/Department School of Aerospace, Transport and Manufacturing
Based at Cranfield Campus, Cranfield, Bedfordshire
Hours of work 37 hours per week, normally worked Monday to Friday. Flexible working will be considered.
Contract type Fixed term contract
Fixed Term Period 3 Years
Salary £33,309 to £37,127 per annum
Apply by 30/07/2020

Role Description

Cranfield University SATM Centre for Autonomous and Cyberphysical Systems welcomes applications from reinforcement learning early-career academics.

As the UK’s only exclusively postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships is creating leaders in technology and management globally. Our distinctive expertise is in our deep understanding of technology and management and how these work together to benefit the world.

Our people are our most valuable resource and everyone has a role to play in shaping the future of our university, developing our learners, and transforming the businesses we work with. Learn more about Cranfield and our unique impact here. Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here.

The School of Aerospace, Transport and Manufacturing (SATM) is a leading provider of postgraduate level engineering education, research and technology support to individuals and organisations. At the forefront of aerospace, manufacturing and transport systems technology and management for over 70 years, we deliver multi-disciplinary solutions to the complex challenges facing industry. SATM is consistently ranked top 50 globally on QS and ARWU, leading the research in AI and digital big data for aerospace and automation.

Our reputation for leading in the field of autonomous and space systems, applied artificial intelligence and signal processing has been established through more than thirty years of research into this field. We cover all types of autonomous vehicles including airborne, ground and marine as well as space.

As a Research Fellow you will contribute to the academic research and post-graduate teaching activities of the Centre for Autonomous and Cyberphysical Systems, especially concerning the newly formed Applied Artificial Intelligence research group. You will be expected to collaborate with the existing staff working in the area and have communications and meetings with our collaborators within the university or in other international universities.

You will be educated to doctoral level (or close to completion) in a relevant machine learning subject and have experience of management research using both qualitative and quantitative methods. With excellent communication skills, you will have expertise in reinforcement learning, with particular relevance in its engineering applications. Further information can be found by visiting https://www.cranfield.ac.uk/themes/aerospace

In return, the successful applicant will have exciting opportunities for career development in this key position, and to be at the forefront of world leading research and education, joining a supportive team and environment.

At Cranfield we value Diversity and Inclusion, and aim to create and maintain a culture in which everyone can work and study together harmoniously with dignity and respect and realise their full potential. To further demonstrate our commitment to progressing gender diversity in STEM, we are members of WES & Working Families, and sponsors of International Women in Engineering Day.

Our equal opportunities and diversity monitoring has shown that women and minority ethnic groups are currently underrepresented within the university and so we actively encourage applications from eligible candidates from these groups.

We actively consider flexible working options such as part-time, compressed or flexible hours and/or an element of homeworking, and commit to exploring the possibilities for each role. Find out more here.

For an informal discussion, please contact Prof. Weisi Guo, Professor of Human Machine Intelligence, on (T); +44 (0)1234 75 8304 or (E); weisi.guo@cranfield.ac.uk; or Dr. Luca Zanotti Fragonara, Lecturer in Structural Dynamics, on +44 (0)1234 750111 Ext. 5280 or (E); l.zanottifragonara@cranfield.ac.uk.

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