Research Assistant/Associate in Statistics

Location
Newcastle, GB
Salary
£28,331.00 - £40,322.00
Posted
Jun 24, 2020
Closes
Jul 24, 2020
Ref
4847
Contract Type
Full Time
Job Type
Technican

We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of England. Attracting and retaining high-calibre people is fundamental to our continued success.

Salary: £28,331.00 - £40,322.00
Closing Date: 26 July 2020

The role

We are seeking to appoint a Research Assistant/Associate in Statistics to work on the project Streaming data modelling for real-time monitoring and forecasting. This project is funded by the Alan Turing Institute (the UK's National Institute for Data Science and Artificial Intelligence) and is being led by a research team from Newcastle University who will develop an international leading capability for the (near) real-time analysis of streaming data. This Turing project will offer the opportunity for collaboration with other Turing projects and researchers at Newcastle and elsewhere.

You will possess a PhD in Statistics or a closely related discipline (awarded or in submission); expertise in Bayesian inference and ccomputationally intensive inferential methodology; track record of research in computational Bayesian statistics and developing efficient programs for statistical computing.

You will have excellent statistical computing skills, including familiarity with modern statistical tools and libraries; strong programming skills in R and an efficient compiled language like C/C++ or Java/Scala; excellent written and oral communication skills; effective time management skills.

This is a full time, fixed term position, available for the duration of 12 months.

You will be supervised by Professor Darren Wilkinson, Dr Andrew Golightly and Dr Sarah Heaps, and will be based in the School of Mathematics, Statistics and Physics.

For informal enquiries contact Professor Darren Wilkinson (darren.wilkinson@ncl.ac.uk) or Dr Andrew Golightly (andrew.golightly@ncl.ac.uk).

Key Accountabilities

• To develop spatio-temporal models that integrate data sources from streaming data networks (e.g. smart city data), collected at a variety of spatial resolutions and temporal frequencies.
• To develop scalable implementations of existing state-of-the-art sequential Bayesian methods (e.g. particle learning, nested particle filters, ensemble Kalman filter) and stress test in several synthetic data scenarios.
• To develop novel online Bayesian methods for both static and dynamic model parameters, by exploiting the use of surrogate models which admit a fixed computational cost for assimilating each new observation.
• To use the best performing inference scheme for fitting the models to data, and generating predictions, in (near) real time.
• The use of techniques from functional and probabilistic programming for the development of composable models and algorithms, and their deployment in modern streaming data frameworks.
• Application of the above techniques to streaming data with healthcare applications.
• To disseminate research through publication in leading peer-reviewed journals and through presentation at international conferences and scientific meetings.

The Person (Essential)

Knowledge, Skills and Experience
• Expertise in Bayesian inference.
• Expertise in computationally intensive inferential methodology
• Excellent statistical computing skills, including familiarity with modern statistical tools and libraries
• Strong programming skills in both R and an efficient compiled language such as C/C++ or Java/Scala
• Track record of research in computational, Bayesian statistics
• Track record of development of efficient programs for statistical computing, optimised to minimise execution time
• Effective time management skills
• Excellent written and oral communication skills

Desirable
• Knowledge and experience of time series and/or spatio-temporal modelling
• Knowledge of Bayesian hierarchical modelling
• Knowledge and experience of computational methods for real-time or fast inference
• Skills in the management, processing and analysis of large, heterogeneous data sets
• Familiarity with streaming data frameworks
• Familiarity with functional and/or probabilistic programming
• Unix/Linux skills, including shell programming
• Version control systems, such as Git or Subversion
• Track record of research publication in internationally recognised journals
• Track record of conducting cross-disciplinary research

Attributes and Behaviour
• Comfortable working both individually and as part of a team
• Ability to interact with researchers with different backgrounds, at all levels, from across the University
• Willingness to take initiative and self-start

Qualifications
• To have (or be close to obtaining) a PhD in Statistics or a closely related discipline. Preferably involving temporal and/or spatio-temporal modelling and/or computationally intensive inferential methodology.

Newcastle University is committed to being a fully inclusive Global University which actively recruits, supports and retains staff from all sectors of society. We value diversity as well as celebrate, support and thrive on the contributions of all our employees and the communities they represent. We are proud to be an equal opportunities employer and encourage applications from everybody, regardless of race, sex, ethnicity, religion, nationality, sexual orientation, age, disability, gender identity, marital status/civil partnership, pregnancy and maternity, as well as being open to flexible working practices.

The University holds a silver Athena SWAN award in recognition of our good employment practices for the advancement of gender equality. The University also holds the HR Excellence in Research award for our work to support the career development of our researchers, and is a member of the Euraxess initiative supporting researchers in Europe.

Please be advised that due to the minimum salary thresholds imposed by the UKVI, this post may not qualify for University sponsorship under Tier 2 of the points based system
Requisition ID: 4847