Closed-Loop Data Science
We are advertising the following vacancy:
1 Post-Doctoral Research Associate/Fellow position in Computing Science on Data Systems
To make a leading contribution to the EPSRC funded project “Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics” coordinated by PI Prof. Roderick Murray-Smith, working with Dr Christos Anagnostopoulos (line manager) and Dr Nikos Ntarmos
The postdoctoral researcher will contribute to our research towards Machine Learning, explainable ML models and Exploratory Data Analysis. ML methods are often used to guide the exploratory data analysis over distributed data systems and to extract, infer and explain information from the data, as part of a data analytics pipeline. These predictions often inadvertently lead to changes in the behaviour of both systems and users (analysts, data scientists), thus potentially invalidating future predictions. Such closed loop effects have so far gone largely unaddressed, resulting in uncertain and unexpected results in practice. This is a huge problem for all associated stakeholders, as it affects both the operational characteristics (stability, scalability, performance) of the data system itself, and the accuracy and perceived value of predictive and inferential analytics on its data. The successful candidate is then expected to develop and experiment with sophisticated ML explainable algorithms, novel exploratory analytics methods and techniques to address these issues, building on results from the fields of adaptive/intermittent closed loop control, machine learning, and statistical learning, as applied to large-scale data systems. The successful candidate will further be able to closely collaborate with our industrial partners (including but not limited to Telefonica, MoodAgent, BBC, Aegean Airlines) to apply our research results in real-world settings.
This position offers:
- An exciting opportunity for inter-disciplinary and highly impactful research.
- Mentoring and support from world-leading academic staff and industry professionals.
- The freedom to determine and manage your own exciting and complex programme of research.
- Access to state-of-the-art computing facilities and real-world datasets/workloads.
- Opportunities for further funding/continuation of employment at the University of Glasgow via joint authorship of research proposals and internships/secondments at our industrial partners.
The post requires expert knowledge in applying machine learning to large-scale data systems and a strong background in mathematical analysis. The ideal candidate will have substantial research experience and publication record in high quality venues and journals in the areas of ML/AI and data analytics systems (e.g., NeurIPS, DSAA, ICML, JMLR, KDD, PKDD, ICDE, ICDM, TKDD, TKDE).
For more details and to apply for this post, please navigate to https://www.jobs.ac.uk/job/CAC233/research-associate-fellow
- Reference Number: 038185
- Location Gilmorehill Campus / Main Building
- College / Service COLLEGE OF SCIENCE & ENGINEERING
- Department SCHOOL OF COMPUTING SCIENCE
- Job Family Research And Teaching
- Position Type Full Time
- Salary Range level 7 (£35,845 - £40,322 per annum) or 8 (£44,045 - £51,034 per annum).