Postdoctoral Research Fellow in Data Analytics and Machine Learning (Fixed Term) (COS616)

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Job description

We seek to employ as soon as possible a highly motivated Postdoctoral Research Fellow that will be working on a recently awarded prestigious Natural Environment Research Council (NERC) grant related to the project entitled “Engineering Transformation for the Integration of Sensor Networks”. You should hold a good honours degree (mathematics, computer science, engineering or life sciences) and a PhD (or a PhD Pending or equivalent) , and should be able to demonstrate a track record in at least one of the following fields:

- Data Analytics

- Statistical Modelling for Environmental Data

- Machine Learning for time-series and other complex data

- Deep Learning

- Programming in Python (and frameworks such as Tensorflow, PyTorch, etc.), R, or C++

Once in post, you will be working with Dr Georgios Leontidis (Principal Investigator) on the research project mentioned above and also with our external collaborators. The main role will be a) to collate data generated/provided by our main collaborator i.e. the NERC Centre for Ecology and Hydrology and b) to develop algorithms/techniques for - real-time - analysis of large amounts of cross-network data for quality control (QC) and gap filling (missing data). We will apply deep learning and other machine learning techniques to rainfall data and compare with existing QC methods to raingauge data for multiple networks. A major task will be to develop techniques for gap filling of missing data, i.e. data imputation, in order to accommodate the analysis of incomplete datasets. External collaborators include the NERC Centre for Ecology and Hydrology and Oxford University. It is also expected to have a direct and close collaboration with other researchers in the project.

A generous budget is available to cover travel and subsistence costs to participate in meetings with our collaborators, networking events and conferences. In addition, state-of-the-art computer equipment is available for accelerated analytics (Multi-GPU server and PC).

The successful candidate will be a member of the Machine Learning Research Group (Mlearn - The Mlearn is a rapidly expanding group, part of the School of Computer Science at the University of Lincoln and specialises in the development of machine learning, neural networks, deep learning techniques, decision-making systems, signal processing and big data across various application fields such as agri-food, energy, environment and other industrial applications. We provide a highly-dynamic inter-disciplinary research environment with a broad range of collaboration opportunities. In this project, you will have access to various sources and knowledge within the University, as well as provided by other partners of the project.

The University of Lincoln is a forward-thinking, ambitious institution and you will be working in the heart of a thriving, beautiful, safe and friendly city. The School provides a stimulating environment for academic research, and is located on the picturesque waterfront campus in the historic and vibrant city of Lincoln. The University has just announced a £130M investment programme, a significant part of which is being invested in new, purpose-built facilities for the School of Computer Science. Lincoln itself is a small but fast growing city in the east-midlands. It offers a fantastic life quality given by moderate living costs, a medieval city centre including a famous cathedral and a beautiful ancient canal system that is still in use by many house boats nowadays.

If you would like to know more about this opportunity, please contact Dr Georgios Leontidis (Senior Lecturer in Computer Science,

Closing Date: 13 Feb 2019
Category: Research

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