The Faculty of Engineering and Physical Sciences at the University of Leeds calls applications for the research Fellowship in Functional Data Analysis at the School of Mathematics. The Research Fellow will join a project in statistical modeling and machine learning. Moreover, the project aims to develop a state-of-the-art multivariate extreme value model and package it into a user-friendly commercial prototype software solution. The fellow will work in close contact with Dr. Leonid Bogachev (PI) and Professor Jeanine Houwing-Duistermaat. Moreover, the position will work on the Department of Statistics where s/he will collaborate with the Modern Applied Statistics and Statistical Methodology and Probability research groups, as well as the Leeds Institute for Data Analytics (LIDA).

Responsibilities 

The incumbent of Research Fellowship in Functional Data Analysis at the University of  Leeds will be

• Designing, planning and conducting a program of investigation, in consultation with Dr. Bogachev and Professor Houwing-Duistermaat.
• Generating and pursuing independent and original research ideas in Functional Data Analysis and its applications to machine learning and data analytics.
• Developing research objectives and proposals as well as contributing to setting the direction of the project and team including preparing proposals for funding in collaboration with colleagues.
• Similarly, developing and prototyping new statistical and machine learning techniques that can be used by other researchers, practitioners, and industries.
• Evaluating methods and techniques used and results obtained by other researchers and relating such evaluations appropriately to your own work.
• Preparing papers, arising from the research on the project, for publication in leading international journals and also disseminating research results through other recognized forms of output.
• Also, working both independently and also as part of a larger team of researchers, engaging in knowledge-transfer activities where appropriate and feasible.
• Maintaining the continuing professional development and acting as a mentor to less experienced colleagues as appropriate.
• Furthermore, contributing to the training of both undergraduate and postgraduate students, including assisting with the supervision of projects in areas relevant to the project.

Requirements 

The ideal candidate will  have

• Doctoral qualification (or submission of a thesis before the start date) in Statistics, Applied Statistics, Data Science, Data Analytics, or a closely related discipline.
• Strong background and experience in statistical modeling and data analysis. Similarly, familiarity with multivariate statistics, functional data analysis, and machine learning;
• Competence in statistical computing including R or Python programming.
• Excellent time management and planning skills, with the ability to meet tight deadlines; manage competing demands and work effectively under pressure without close support.
• A proven track record of peer-reviewed publications in high impact factor journals.
• Excellent written and verbal communication skills including presentation skills as well as ability to work well both individually and in a team.
• Furthermore, a strong commitment to your own continuous professional development.

Salary/Compensation

The University of Leeds offers an attractive salary of £33,797 to £40,322 per annum to the appointee of the Research Fellowship in Functional Data Analysis.

University of Leeds

The University of Leeds is a public research university in Leeds, England. Having the motto of ‘And knowledge will be increased’; the university establishment dates to 1874 as the Yorkshire College of Science. It lies in the top 20 in the world for graduate employability. Moreover, the university has affiliations with 6 Nobel Laureates. Furthermore, Leeds was in the 19th position amongst multi-faculty institutions in the UK for the quality (GPA) of its research. Above all, the university is in 93rd position in the QS World University Rankings 2019. Also, it is the fifth-largest university in the UK in terms of student numbers.

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