Salary range:
Circa £37,546 - £39,318
FTE:
1.0
Term:
Fixed (36 Months)
Closing Date: 16 April 2021
The Department of Electronic and Electrical Engineering (EEE) within the Faculty of Engineering is looking for 3 highly motivated early stage researchers (ESRs) to engage in cutting edge interdisciplinary AI research combined with a structured curriculum of training activities. The successful candidates will be fully integrated within the European Commission MSCA GECKO (https://gecko-project.eu) International Training Network (ITN) to work with 12 other ESRs within an interdisciplinary consortium of nine European academic and industrial institutions, with support from 6 European industrial partners and Stanford University.
You will be trained in an international, inter-disciplinary academic and industrial environment through state-of-the-art research, GECKO training schools, and secondments to academic and industrial institutions. Selected candidates will enter 36-month work contract (full-time) with University of Strathclyde and will also be seconded to other GECKO academic and industrial partners.
You will be enrolled within the EEE PhD programme at the University of Strathclyde. Besides a highly competitive salary, mobility and family allowance (restrictions apply), the recruitment package includes excellent research and training support as per MSCA ITN guidelines.
GECKO will target interpretable and explainable Artificial Intelligence (AI) and explore alternative methods to build machine learning models drawing on the latest developments in information and social sciences. The focus of GECKO will be on AI design that is robust, from both technical and social perspectives, since even with good intentions, AI systems can cause unintentional harm. With this in mind, GECKO will focus on sustainability in relation to end users, where the decisions made by AI can significantly and directly affect people.
The PhD projects will exploit model visualisation tools and response to dynamics of collected data to understand the reasoning behind machine learning outcomes; information extraction methods to meet privacy and trust requirements; and novel graphical inference and signal and information processing techniques to acquire understanding how deep learning methods transform input data into outcome recommendations. The successful candidates will interact closely with social science teams to integrate human/social elements in the machine learning models, including biases, inclusion, accountability, and provide computational methods to understand social phenomena, studying causal inference in social systems.
For informal enquiries, please contact Dr Vladimir Stankovic, Reader, vladimir.stankovic@strath.ac.uk
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