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Senior Research Associate

Mathematics & Statistics
Salary:   £34,804 to £40,322
Closing Date:   Monday 16 March 2020
Interview Date:   Friday 27 March 2020
Reference:  A2960

An exciting opportunity exists for a researcher to develop machine learning algorithms that combine statistical and process models for novel soil sensors through a collaborative “Signals in the Soil” research project. The project, funded by NERC and NSF (https://gtr.ukri.org/projects?ref=NE%2FT012307%2F1), is a joint venture between Lancaster University, University of Manchester and University of Colorado Boulder. This is an exciting opportunity to leverage insight from the environmental sciences to inspire the next generation of statistical machine learning approaches, and to gain experience of working with international partners while part of the UK’s leading department for industrially-relevant statistics and operations research.

You will be required to liaise with the environmental scientists and engineers on the project team who will share models and expertise to help you gain the necessary insights into the problem. The algorithms you will develop will need to contend with multiple data collection sites, complex data structures, mixed sampling frequencies and incomplete data. You will develop new statistical algorithms and publish them in statistical journals and/or conferences as well as contributing to publications in the environmental sciences. You will also be required to develop software to be published to the scientific community.

You should have completed, or be close to completing, a PhD in statistics, machine learning, or a related discipline. Experience of developing algorithms and associated software is desirable but not essential. However, it is imperative that you have a demonstrable ability to both carry out and publish academic research, and to develop research-level software. Please explain in your cover letter your suitability for this role and how you will contribute to the Department

The position is available for immediate start, or at an agreed time, full time for 30 months.  For this role we are open to discussing the possibility of reduced hours, job share, remote working, flexible start and finish times, or compressed hours.

Interested candidates are strongly advised to contact Dr.Rebecca Killick r.killick@lancs.ac.uk or Dr Chris Nemeth c.nemeth@lancs.ac.uk in advance of making an application.

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