4-year PhD Studentship in Knowledge Engineering

Project: “FAIR synchrotron data, from theory to implementation: understanding and evaluating the technical, social and policy implications”

Supervisors: Professor Susanna-Assunta Sansone and Dr Philippe Rocca-Serra, Data Readiness Group (https://datareadiness.eng.ox.ac.uk ), Department of Engineering Science, University of Oxford; Professor Steve Collins, Diamond Light Source Ltd.

Project: The details will be defined with the successful candidate, also according to their skills and ideas.

To meet expectations of governments and funders, new mechanisms are needed to ensure greater transparency and reuse of research data. For scalable, effective and trustworthy data-driven science, we need new technological and social infrastructure, as well as cultural and policy changes. The widely adopted FAIR Principles cover four key features of research data, Findability, Accessibility, Interoperability, and Reusability, which are central to open science and public confidence in science. The FAIR Principles have become fundamental to progress in research and are increasingly demanded by funding agencies Worldwide.

This DPhil project is a collaboration between Diamond Light Source and Oxford University, set to understand the implications of adopting the FAIR Principles and the effects of its implementation on synchrotron data, covering a broad range of studies in physics, chemistry, engineering and life sciences. The student will spend time (50/50) in Oxford and at Diamond, where they will learn about the latest advancement in the FAIR data ecosystem, and synchrotron science, respectively. Professor Tony Hey, Chief Data Scientist at STFC, will serve as Advisor.

The problem is that data rarely follow FAIR Principles, and require extensive preparation before the researchers can begin to use the data and answer sophisticated research questions. This DPhil project will (i) examine the current data structure, the types of research question being asked, the evolving landscape of metadata standards, semantic web technologies, and the latest data representation and discovery; (ii) define and prototype how to move from the current manually-focused, time-consuming and error-prone operations to a streamlined, unambiguous and AI-ready framework; and (iii) guide future Diamond data management policy towards achieving goals of better data for better science.

Eligibility

The studentship is open to Home classified students only. Full details of the EPSRC eligibility requirements : https://www.ukri.org/councils/esrc/career-and-skills-development/funding-for-postgraduate-training/eligibility-for-studentship-funding/

Award Value

Course fees are covered at the level set for the UK student (c. £8620 p.a. in 2022-23). The stipend is at least £18,062 per annum.

Candidate Requirements:

Excellent academic potential and an enthusiasm for understanding organizational dynamics, open science/innovation, and data management. A first-class degree in science or computer science is desirable.

Experience of data science and digital economy; some familiarity with social science research methods; a high degree of independence, excellent communication skills, and fluency in English.

Application Procedure

Informal enquiries to Professor Sansone ([email protected] ).

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website .

Please quote 23ENGIN_SS in all correspondence and in your graduate application.

Application deadline:  noon on 9 December 2022  

Start date: April 2023