Lectureship in Data Science and Earth and Environmental Analytics

The University of Manchester

Applications are invited for a Lectureship in Data Science, Earth andEnvironmental Analytics for appointment in 2022.

We are seeking to appoint a data scientist with an outstanding record of research, in particular, in application of data science methodologies to complex Earth and environmental challenges.  At Manchester, you will join the Department of Earth and Environmental Science (DEES) at a timely moment of expansion in digital environmental research. The global Earth and environmental research community, and the research base in DEES, has developed a plethora of observational and modelling platforms designed to better understand the evolution of our Earth and environment and our role within in. This has led to an explosion in the availability of large and complex data sets from diverse sources (e.g. environmental monitoring; subsurface imaging; remote sensing; climate modelling; social media; and contributions from citizen science). Environmental and sustainability challenges are inherently global, and the volume of data that need to be incorporated into decision making at this scale means that data science and AI will be crucial in providing the robust, decision-quality information that is required. This is especially true in the case of the Sustainable Development Goals (SDGs) for which quality, accessible, timely and reliable disaggregated data is essential for understanding the scale of the challenges and in measuring progress. The primary aim of developing data driven platforms is to understand and respond to the complex interactions between the subsurface, environment, climate, natural ecosystems, human social and economic systems, and health.

There is also a growing need for improved data science skills in graduates from Earth and environmental domains. This includes a demonstrable understanding of core concepts in traditional and emerging machine learning, general computing and broader data science skillsets from data pre-processing to visualisation and ethics.

In this post you would contribute to the delivery of teaching on the Earth and Environmental Analytics pathway of a new Data Science MSc at Manchester University. The Earth and Environmental Analytics pathway adds domain relevant courses to supplement core training in computational skills, data analytical skills, data stewardship skills and project design skills. You will also help embed data science skills in our evolving undergraduate programme which is already developing modules that provide training on mathematics, programming and numerical modelling for environmental science students.

Besides contributing to the day-to-day running of this pathway, including the supervision of projects in the 3rd semester, you will also be required to develop and deliver domain relevant courses to supplement core training in computational skills, data analytical skills, data stewardship skills and project design skills. In addition to expanding our offerings on postgraduate programmes, further requirements include embedding data science skills in our evolving undergraduate programmes, where desired, which are providing training on mathematics, programming and numerical modelling for environmental science.

The successful candidate is expected to have a track record of publications in data science, in the relevant journals of your field. This includes a record of recent publications demonstrating expertise in broader data science  relevant to Earth and environmental sciences which might include, for example, climate change, air quality, ecology, biology, geography and/or oceanography.

It is expected that, in the longer term, this position would also further fertilise collaborations across a number of scientific domains that are of interest to both siloed and cross UKRI/ international areas of growth. For example, these can include, but are not limited to:

  • Climate Change e.g. resilient cities, clean power, smart transport options, nature-based solutions, sustainable production and consumption, smart cities and homes
  • Biodiversity and Conservation e.g. habitat protection and restoration, patterns and drivers of biodiversity change, realizing natural capital, and nature-based solutions to biodiversity and climate mitigation.
  • Mitigation of Anthropocene environmental challenges, such as carbon capture/storage and environmental recovery following extraction of earth materials
  • Earth system data science, examining interactions between ocean, land, ice and air
  • Subsurface processes e.g. analysis of geophysical, remote sensing or genomic data
  • Palaeoanalytics, applying data science and machine learning to evolutionary studies
  • Natural Hazards e.g. prediction, forecasting and management of hazards, early warning systems, resilient infrastructure.
  • Environment and Health e.g. air quality, water and sanitisation, social and environmental inequalities, urban environments.
  • Environmental Digital Twins. Digital twins present a tremendous opportunity for utilising near to real-time data to predict rapid evaluations of potential interventions or environmental change.

As an equal opportunities employer we welcome applicants from all sections of the community regardless of age, sex, gender (or gender identity), ethnicity, disability, sexual orientation and transgender status.  All appointments are made on merit.

Our University is positive about flexible working – you can find out more here

Blended working arrangements may be considered

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Enquiries about the vacancy, shortlisting and interviews:

Professor Mike Burton, Email: mike.burton@manchester.ac.uk

Prof David ToppingEmail: david.topping@manchester.ac.uk

General enquiries:

Email: hrservices@manchester.ac.uk

Technical support:


This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.

To apply for this job please visit www.jobs.manchester.ac.uk.