Senior Research Fellow in Modelling

University of Nottingham

United Kingdom

Senior Research Fellow in Modelling (Fixed-term)

Reference: SCI145620

Closing Date: Thursday, 25th June 2020

Job Type: Research & Teaching

Department: Agricultural & Environmental Sciences, Biosciences

Salary: £40322 to £49553 per annum (pro-rata if applicable) depending on skills and experience. Salary progression beyond this scale is subject to performance.

We are looking to appoint a Senior Research Fellow (Modelling) with post-doctoral or equivalent professional experience to join a multidisciplinary and international team for a project that aims to deliver web-based tools to support policy, management and other interventions to reduce risks of micronutrient deficiency in the global south.

The MAPS project (Micronutrient Action Policy Support), a major investment by the Bill & Melinda Gates Foundation (BMGF), aims to develop an online tool to enable a range of stakeholders to engage with data on human dietary micronutrient supply and status, and the factors that influence risk of deficiency.  The tool will allow exploration of spatial variations in these factors at whatever spatial scale the available data will support, and the linking of data with other modelling tools to allow the assessment of policies and interventions.

This role is for a statistical modeller to support this project, and to work with colleagues in nutrition, agricultural and environmental sciences, system development, intervention modelling and data management.  The role holder will develop tools, for the R platform, which can be used within the MAPS framework to address its objectives, and will contribute to the wider project through tasks such as the evaluation of available data streams, the use of elicitation methods to engage with stakeholders, and the development of innovative and flexible approaches to the visualization and communication of uncertain information.

All this work will entail collaboration with system developers and with specialists in nutrition, agricultural science, geochemistry and food systems.  A capacity to communicate with collaborators and to contribute proactively to the project goals is critical, along with a willingness to engage with stakeholders.  At the same time the project will offer the opportunity to undertake research, to develop novel ideas and to publish these collaboratively.

Candidates must have: 

  • Excellent skills in statistical modelling with data, including those from probability samples in a design-based setting, and the use of model-based spatial statistical methods.
  • Experience of coding for the R platform, beyond the use of standard packages, including the development of functions to implement new methods.
  • Excellent team-working skills, particularly in a cross-disciplinary setting.
  • Excellent written and spoken language skills (English).
  • Experience of cross-disciplinary working.
  • PhD in relevant discipline (or equivalent professional experience).
  • Ability to travel overseas

Experience in the following would be advantageous:

  • Developing approaches to visualization and communication of statistical results
  • Collaboration in key areas of the project including nutrition, public health and agricultural and environmental sciences 
  • The process of eliciting quantitative information from experts by formal methods.

This full-time (36.25 hours per week) post is fixed-term until 31 October 2023. Job share arrangements may be considered.

Informal enquiries may be addressed to Prof Murray Lark, email: Please note that applications sent directly to this email address will not be accepted.

Our University has always been a supportive, inclusive, caring and positive community. We warmly welcome those of different cultures, ethnicities and beliefs – indeed this very diversity is vital to our success, it is fundamental to our values and enriches life on campus. We welcome applications from UK, Europe and from across the globe. For more information on the support we offer our international colleagues, visit;

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