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Haida Liang

6 months ago

AI-Aided Data-Driven Study of Historical Parchments: Making, Degradation, and Origin Nottingham Trent University in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD Project (Students Worldwide)

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Nottingham

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Keywords

Computer Science
Machine Learning
Chemistry
Experimental Physics
Materials Science
Biology
Conservation Biology
Analytical Chemistry
Artificial Intelligence
Archaeology
Optical Coherence Tomography
Hyperspectral Imaging
Noninvasive Imaging
Econometric
Statistics
Bioinformatic
Physics

About this position

This PhD project offers a unique opportunity to explore the making, degradation, and origin of historical parchments using advanced AI-aided, data-driven approaches. Parchments, made from treated animal skins, have served as the primary writing material in Europe and West Asia from the 4th to the 15th century, preserving invaluable records of civilisation. Understanding their origin, manufacturing processes, and degradation mechanisms is crucial for heritage conservation and manuscript dating.

The research will focus on the identification of animal species (calf, sheep, goat) used in parchment production, leveraging biocodicology and both invasive (ZooMS, eZooMS) and non-invasive (reflectance spectroscopy, hyperspectral imaging, optical coherence tomography) analytical techniques. Machine learning and AI methods will be developed to enhance species identification and to disentangle complex multimodal imaging data, capturing information about animal origin, age, manufacturing differences, and degradation states.

Students will work with a large collection of parchment manuscripts, employing non-invasive imaging to record statistical variations and train robust AI models. Comparative studies will be conducted on manuscripts from Belgium and British archives, representing different manufacturing traditions and exposure conditions. Reference proteomics analyses will be used for verification and calibration.

The project is based in the Imaging and Sensing for Archaeology, Art history and Conservation (ISAAC) Lab at Nottingham Trent University and the Artificial Intelligence for Cultural Heritage (PHOENIX) collaborative research project at the University of Namur, Belgium. The studentship is jointly funded by both institutions, covering tuition fees and stipend, and the successful candidate will be registered at both universities.

Applicants should have a background in physical sciences or bioinformatics, with a strong interest in heritage science, conservation, and archaeological science. The project welcomes candidates from diverse backgrounds and encourages applications from underrepresented communities. The research environment is inclusive and supportive, offering access to cutting-edge facilities and interdisciplinary expertise.

Application deadline is June 15, 2026. For further information, visit the ISAAC Lab and PHOENIX project websites, or contact the supervisory team. Apply via the Nottingham Trent University applications page, ensuring you meet the eligibility criteria.

Funding details

Funded PhD Project (Students Worldwide)

What's required

Applicants should have a background in physical sciences or bioinformatics and demonstrate interest in heritage science, conservation science, or archaeological science. Motivation and relevant skills in machine learning, analytical chemistry, and imaging techniques are desirable. Eligibility criteria are outlined on the applications page. No specific GPA or language test requirements are mentioned.

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Professors

Professor at School of Science & Technology

University Name
Country flag

United Kingdom

Research Interests

Mineralogy

Archaeometry

Optical Imaging

Hyperspectral Imaging

Time-resolved Spectroscopy

Professor at School of Science & Technology

University Name
Country flag

United Kingdom

Research Interests

Analytical Chemistry

Statistics

Machine Learning

Optical Coherence Tomography

Archaeology

Professor at School of Science & Technology

University Name
Country flag

United Kingdom

Research Interests

Conservation Biology

Statistics

Machine Learning

Experimental Physics

Materials Science