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MHRA Research Associate

King's College London - English Language & Literature

Location: London
Salary: £45,031 per annum pro rata, including London Weighting Allowance
Hours: Part Time
Contract Type: Fixed-Term/Contract
Placed On: 26th June 2026
Closes: 9th July 2026
Job Ref: 150865

About the role:

We wish to appoint a 0.5 FTE Research Associate for a fixed term of 12 months (1 October 2026 to 30 September 2027) to work on attribution questions relating to early modern manuscript materials in the Burley Manuscript, held at Leicester Record Office. The post is funded by the Modern Humanities Research Association and forms part of a defined phase of work connected to The Oxford Correspondence of John Donne and the co-authored monograph project Donne in Venice.

Working with Dr Daniel Smith, the postholder will undertake stylometric, corpus-based, and computational authorship-attribution analysis of selected materials from the Burley Manuscript, working from existing transcripts and in dialogue with an already assembled corpus of Donne’s prose correspondence. The role is designed to bring computational and statistical approaches to bear on a longstanding set of attribution problems, complementing existing palaeographical, bibliographical, historical, linguistic, and editorial research.

Working mainly from King’s Strand campus, the successful candidate will help to prepare, review, analyse, and document textual corpora and datasets; identify and apply appropriate methods for testing attribution hypotheses; and communicate the results of this work to the wider editorial team. The postholder will contribute to a structured analytical report, methodological documentation, and research outputs arising from the Fellowship, including material relevant to Donne in Venice and a possible stand-alone article on the methodology and findings.

This is a part-time, 12-month post at 50% FTE.

About you:

Essential criteria

  • PhD in a relevant subject area in English Literature, early modern studies, textual scholarship, digital humanities, corpus linguistics, computational literary studies, or another relevant field.
  • Knowledge of early modern literary, textual, manuscript, or historical studies, with the ability to apply this knowledge to research on seventeenth-century manuscript or printed materials.
  • Experience of corpus-based, computational, or quantitative approaches to textual analysis, relevant to authorship attribution, stylometry, corpus linguistics, or related forms of textual research.
  • Ability to prepare, manage, and document textual corpora or datasets for research purposes, including attention to accuracy, consistency, reproducibility, and appropriate methodological recording.
  • Ability to analyse and interpret complex textual evidence, bringing computational findings into dialogue with literary, historical, linguistic, bibliographical, palaeographical, or editorial evidence.
  • Excellent written and oral communication skills, including the ability to communicate specialist or technical material clearly to academic audiences.
  • Ability to work collaboratively as part of a research team, including contributing constructively to meetings, shared decision-making, and collective research outputs.
  • Ability to plan and manage own research activity effectively, working to agreed objectives, milestones, and deadlines within a fixed-term, part-time appointment.

Desirable criteria

  • Experience of applying authorship-attribution, stylometric, corpus-linguistic, or related computational methods to a live research problem involving contested, uncertain, or unattributed textual materials.
  • Experience of working with early modern manuscripts, archival materials, palaeography, secretary hand, or scholarly transcription.
  • Knowledge of John Donne, early modern epistolary culture, manuscript circulation, diplomatic writing, or seventeenth-century literary and political networks.
  • Experience of textual editing, scholarly annotation, editorial workflows, or work on a major collaborative research project.
  • Experience of using programming languages, statistical software, or relevant digital tools for textual analysis, such as R, Python, Stylo, or comparable platforms.
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