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Research Associate

Remote:

Full Remote

Contract:

Full time

Experience:

Mid-level (2-5 years)

Work from:

United Kingdom

Offer summary

Qualifications:

Doctorate in History or related field, Expertise in machine learning methods, Experience with computer vision techniques, Familiarity with open-source development, Candidates nearing PhD completion welcome.

Key responsabilities:

Collaborate on landscape change research
Use MapReader software for datasets
Co-design experiments with team members
Assist in publishing datasets and models
Engage with partners from National Parks

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Job description

Applications are invited for aResearch Associate in Computational Environmental Historyto contribute to the Landscape Change and Conservation with MapReader (LCCM) project.

We are looking for a talented historian, digital humanities scholar, or geographer with experience in environmental history, historical geography, the history of cartography, or a related area to join us. AHRC-Lancaster University Impact Acceleration Account, this project is a collaboration with the MapReader team on the Data/Culture project at The Alan Turing Institute and colleagues from UK National Parks. You will work with Dr. Katherine McDonough (Lancaster, Principal Investigator), David Alexander (Peak District National Park) Dr. Kalle Westerling (The Alan Turing Institute), and Rosie Wood (The Alan Turing Institute).

This is a 0.6 FTE appointment for a fixed-term of 5 months starting on 1 November 2024, or as soon as possible after that.

This project brings together an interdisciplinary team to research landscape change from the nineteenth century to today in UK National Parks. Working specifically with the Peak District and South Downs National Parks, the team will use the MapReader software library to create and analyse datasets of specific features (such as field boundaries, trees, or footpaths) in a variety of historical maps shared by the National Library of Scotland. Using computational methods to explore map content, LCCM aims to provide National Parks with information to support planning conservation efforts, including helping to meet biodiversity and net-zero targets.

You will work with the team in using, maintaining, and documenting the open-source MapReader software library for these experiments, co-designing experiments with historians and data scientists. They will assist with openly publishing datasets and models resulting from these experiments.

You will enjoy strong mentorship on future career pathways and funding capture, and get experience working closely with partners at UK National Parks (Peak District and South Downs), the National Library of Scotland, and The Alan Turing Institute (London). The Research Associate will be based in the Department of History at Lancaster University, with opportunities for in-person co-working in London at The Alan Turing Institute.

You will have a doctorate in History or a related field (e.g. Geography, Digital Humanities), and expertise in applying machine learning methods to the analysis of historical documents. Experience using standard computer vision and machine learning methods, and open-source development and documentation are desired. Candidates who have not yet completed the PhD, but otherwise meet the person specification criteria, are welcome to apply.

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