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Vacancies

EPSRC PhD studentship: Artificial intelligence and the future of work, of organisations, and labour markets

For autumn 2018, Imperial Business Analytics are seeking applications for a funded doctoral studentship aligned with the study of AI’s effect on organisations, labour markets and work.

Project description

As advances in machine learning are increasingly productively applied to tasks previously done by humans, the scale-up of innovations that permit automation will have profound effects on not only organisations and labour markets, but on economy and society more broadly. Although these changes have attracted considerable interest from academics and public intellectuals, the methods of data science have not yet been applied to understanding how data science will change the world of work. In particular, we are interested in the impact AI is likely to have on so-called knowledge work because the investment requires to scale automation of these tasks is not limited by investment in physical assets. Currently, our group is pursuing the use of data on organisations and work to develop rigorous models that link features of work to features of algorithms and likely developments in data science to forecast the scale and impact of AI. Specifically, we are producing models that link features of work to features of methods in artificial intelligence to guide (a) the development of methods applicable to automating key tasks and (b) forecasting the pace and scope of these technologies on organisations.

Within this broad topic, we are seeking students with an interest in AI, organisations, and the future of work, organisations, labour markets, and economy and society more broadly. Although you will be expected to develop your own original research for the PhD thesis, we are seeking students who can contribute to the modelling agenda described broadly above, which features both methods for automating work and forecasting the impact of automation.

Because data science requires data, the DSI and Imperial Business Analytics Lab have developed strategic connections with industry partners who have data, and these links will be particularly helpful for this studentship. Drawing on the rich and evolving ecosystem of ties between academics and industry, the successful student will benefit from input and data from a wide range of industries and the successful candidate is expected to work closely with external companies.

Studentship Description

Our Doctoral programme is a full-time, five-year programme. In the first year, you will complete a Master’s in Research (MRes) programme with highly relevant and structured training. During that year you will take compulsory modules in research methodology and subject-specific theory, which will provide you with a theoretical grounding and thorough research training for a solid foundation for your academic career. You will also undertake an individual research project with Imperial Business Analytics, meant as a stepping stone for your PhD research project.

For the remaining four years you will design and conduct your PhD research project in collaboration with Dr Mark Kennedy, Director of Imperial Business Analytics, as part of the wider research programme.

The Doctoral programme at Imperial College Business School is fully funded and this studentship would be co-funded by EPSRC which includes full tuition fees at the Home/EU rate plus a living stipend for up to five years.

Residency Criteria:

Applicants must meet Research Council UK eligibility requirements. This means that applicants should have no restrictions on how long they can stay in the UK, and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For UK and EU applicants, residency is permissible if they have been in the UK for the purposes of full-time education; non-EU applicants would need to demonstrate that the purpose of their residency was not wholly or mainly for the purpose of full-time education.

For more information on the doctoral programme, the entry requirements, and details on how to apply, please visit: https://www.imperial.ac.uk/business-school/programmes/doctoral-degree/

If you are interested in the project specified below, please indicate this in your application. For more information on the opportunity, please contact k.heyworth@imperial.ac.uk.

Application closing date: 8 January 2018 (midnight GMT)

PhD opportunity - Supporting human insight with AI in urban master plan design space exploration

Supporting human insight with AI in urban master plan design space exploration
 
 We have an exciting PhD opportunity with the Data Science Institute and Arup (http://www.arup.com) – a really really interesting company.  If you are interested, please get in touch.  If you know someone who might be interested please pass this email on to them.  Candidates will need a first-class MEng, MSci degree or distinction at MSc. 
 
Supervisors:
Prof Paul Kelly (Software Performance Optimisation Group, Dept of Computing, Imperial College London, http://www.imperial.ac.uk/people/p.kelly)
Dr David Birch (Data Science Institute, Dept of Computing, Imperial College London, http://www.imperial.ac.uk/people/david.birch)
This is a collaborative research project with Alvise Simondetti of Arup’s Foresight team (http://www.driversofchange.com/about/team/alvise-simondetti/).
 
Design is the process of balancing competing concerns – aesthetically and in performance across many objectives, e.g. reducing carbon emissions within a cost budget. Analysing design performance often relies upon computationally intensive analysis models (ray tracing for lighting, computational fluid dynamics for acoustics and pollution dispersal). Such expensive models make exploring the vast design space intractable and frequently only a couple of designs are quantitatively analysed. This means opportunities to shape a better world are missed.
 
The goal of this project is to develop novel tool support for tackling architectural design problems, focusing initially on urban masterplanning.  The key challenge is not outright automation or optimisation, but rather supporting humans in deriving insight, particularly to understand the most profitable and flexible parts of the design space.  We propose to extend our prior work with with Arup in this field with techniques from statistical machine learning, in particular the idea of Kriging (also known as Gaussian process regression) to derive a simplified “proxy” model that can be evaluated quickly.  Such models should help offer instant, interactive design feedback, as well as enabling us to identify the statistically most likely-profitable directions for further exploration, with respect to multiple design objectives.   
 
This project is funded under the EPSRC’s Industrial CASE scheme, which supports collaborative research which is based in the university but benefits from regular contact and guidance from our industry partner, Arup.
 
 
If you’re interested, please email Paul Kelly with your CV with a short covering email explaining why you think this might be for you – as soon as possible.