Lecturer/Reader in Foundations of Data Science
- Employer
- QUEENS UNIVERSITY BELFAST
- Location
- Belfast, Northern Ireland
- Salary
- £33,199 to £50,132 per annum
- Closing date
- 29 Apr 2019
View more
- Academic Discipline
- Mathematics & Statistics, Physical Sciences
- Job Type
- Academic Posts, Readers / Senior Research Fellows, Lecturers / Assistant Professors
- Contract Type
- Permanent
- Hours
- Full Time
Queen’s University Belfast
Lecturer/Reader in Foundations of Data Science
School of Mathematics and Physics
Ref: 19/107294
Queen’s University Belfast is one of the leading universities in the UK and Ireland, with a distinguished heritage and history. With over 24,000 students, 3,700 staff and an annual turnover of some £300m, Queen’s University Belfast plays a unique leadership role in Northern Ireland. As a member of the Russell Group of UK research-intensive universities, Queen’s University Belfast combines excellence in research and education with a student centred ethos.
As part of a major investment in Mathematical Sciences at Queen’s University Belfast, applications are invited for the above post, to be held jointly between the School of Mathematics and Physics and the Institue of Electronics, Communication and Information Technology, to start as soon as practically feasible.
The successful candidate will join a dynamic research environment in a supportive and collegial setting at an exciting time, to develop the Foundations in Data Science and enhance mathematics education at Queen’s University Belfast.
The successful candidate will demonstrate outstanding ability and potential. They will be research-driven and willing to play a decisive role in the Foundations of Data Science research at Queen’s University Belfast, in collaboration with academics within the University and beyond. Successful candidates will be REF returnable within Mathematical Sciences and eligible to become Associate Members of the Institute of Electronics, Communication and Information Technology (ECIT) at Queen’s University Belfast. Preference may be given to candidates specialising in Machine Learning, Combinatorial Optimisation and Applied Graph Theory, but strong candidates from all foundational fields of Data Sciences will be considered and are encouraged to apply.
The successful candidate must have:
At Lecturer level:
- Hold a PhD in Foundations of Data Science
- Evidence of potential to deliver high quality teaching in Mathematics, Statistics and/or Data Science at undergraduate or postgraduate level through the medium of English
- A strong record of publications, commensurate with career stage, in the Foundations of Data Science in peer reviewed/refereed journals that are REF returnable within the Mathematical Sciences Unit of Assessment
At Reader level:
- PhD in the Foundations of Data Science
- Evidence of sustained high quality lecturing in Mathematics, Statistics and/or Data Science at undergraduate or postgraduate level through the medium of English
- Ability to provide strategic academic leadership in programme development and teaching
- A distinguished record of research publications of international excellence, commensurate with past research career, in the Foundations of Data Science, REF returnable at international level
Further information about the School can be found at www.qub.ac.uk/schools/SchoolofMathematicsandPhysics/
Anticipated interview date: Monday 27 May 2019
Salary Scale: Lecturer £36,261 - £50,132 per annum (potential to progress to £53,175 per annum through sustained exceptional contribution)
Reader: £51,630 - £59,828 per annum (potential to progress to £65,361 per annum through sustained exceptional contribution)
Closing date: Monday 29 April 2019
For full job details and essential/desirable criteria please refer to the candidate information link on our website by clicking apply. For further information or assistance contact Human Resources, Queen’s University Belfast, BT7 1NN. Telephone (028) 9097 3044 or email on HR@qub.ac.uk
The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability.
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