NEW 2017! Prestigious Scientia PhD Scholarship available (UNSW Sydney, Australia)
Deadline: 21 July, 2017
Details here: http://web.maths.unsw.edu.au/~lafaye/#opportunities
The UNSW Scientia Ph.D. Scholarship Scheme is the most prestigious and generous scholarship scheme at UNSW. It aims to attract the best and brightest people into strategic research areas. Awardees receive a $50,000 scholarship package for four years, comprising a $40,000 per annum tax-free stipend and a travel and development support package of up to $10,000 per annum. International students also receive a tuition fee scholarship. In addition to this scholarship package, scholars are provided with access to a range of development opportunities across research, teaching and learning and leadership and engagement.
The funded project aims to develop new tools and insights for insurer risk management by combining modern statistical learning (‘data analytics’, ‘big data’, ‘predictive analytics’) techniques with actuarial risk theory. The findings will allow for accurate and equitable rating and measurement of risks, and ultimately contribute to sustainable and equitable protection for policyholders. For equity and stability, insurers must be able to assess their risks accurately. Nowadays, they have access to an increasing number of data sources of very different types, and in finer and finer detail. This interdisciplinary project is concerned with 21st century estimation of insurance risks, and proposes to deal with all of the four V’s of big data: volume, velocity, variety and veracity. The focus will be on the extension of recent statistical analytics including in particular deep learning. Insights developed with this analysis will be further incorporated into concepts from actuarial risk theory.
The supervisory team will comprise Benjamin Avanzi and Bernard Wong (both Associate Professor at the Business School, Risk and Actuarial Studies) and Pierre Lafaye de Micheaux (Senior Lecturer, School of Mathematics and Statistics).
The candidate must have a strong background in statistics/mathematics and good programming skills (preferably in R and C/C++; some experience with Linux would be an asset).
If you are interested, please contact either of us for more details, joining a recent CV and a copy of your academic transcripts