Blätter-Navigation

Angebot 17 von 230 vom 20.12.2024, 13:56

logo

Max-Planck-Institut für evolutionäre Anthropologie - Zentrales Bewerbermanagement

About MPI-EVA
The Max Planck Institute for Evolutionary Anthropology unites scientists with various backgrounds (natural sciences and humanities) whose aim is to investigate the history of humankind from an interdisciplinary perspective with the help of comparative analyses of genes, cultures, cognitive abilities, languages and social systems of past and present human populations as well as those of primates closely related to human beings.

About DLCE
The Department of Linguistic and Cultural Evolution aims to answer big picture questions about the evolution of language and culture. Our focus is on describing and explaining the major patterns of linguistic and cultural variation across the globe. We tackle these questions by developing novel language documentation methods, global linguistic and cultural databases, and analyses using evolutionary theories and computational methods.

Postdoctoral researcher (m/f/d) in the field of Artificial Intelligence and Historical Linguistics

English

Working field:

The project “AI assisted Historical and Comparative Linguistics” aims to investigate the potential of artificial intelligence (AI) to assist and enhance research in historical linguistics. The project will focus on developing AI tools designed to support linguists in handling vast amounts of historical linguistic data, facilitating tasks such as tracking phonological, syntactic, and semantic changes over time, inferring cognates and constructing language phylogenies. By offering more efficient methods for processing large, complex datasets, these AI-powered tools will allow researchers to uncover patterns and trends that may not be easily detectable through manual analysis alone. The emphasis will be on using AI to complement the knowledge and interpretive skills of historical linguists, empowering them to make more informed inferences and pursue new lines of inquiry.

Requirements:

  • A passion for science
  • A PhD in Computer Science, Artificial Intelligence and/or Historical Linguistics
  • Experience with AI technologies such as large language models (e.g., GPT, BERT,…) and machine learning
  • Excellent statistical / computational skills with a knowledge of Python and R
  • Familiarity with computational linguistics tools and frameworks
  • Familiarity with the comparative method in historical linguistics
  • Ability to work in interdisciplinary teams
  • Proficiency in both spoken and written English

What we offer:

  • Full-time position, starting in late spring of 2025, initially limited to two years
  • Salary based on experience according to the German TVöD
  • According to German labor laws, benefits include 30 days holiday per annum plus Bank Holidays and Christmas closure days as well as an attractive pension proposition
  • An inspiring, international, interdisciplinary environment with leading domain scientists at our department and in various Max Planck Institutes and universities
  • Space, freedom, support and resources to do all the things described above

How to apply:

Please submit your application in English, including the following:

  • Cover letter, explaining research experience and reason for interest in this project, along with a proposed research project (max. two pages).
  • Your CV and copies of degree certificates (Bachelor’s/Master’s/PhD diplomas and transcripts)
  • Names and contact information (including e-mail and phone) for 2-3 referees

Please apply online via our online application system (link via the job ad on our career website). Only complete submissions via this link will be taken into consideration. Deadline for applications is 10 January, 2025.

The Max Planck Society is committed to employing individuals with disabilities and especially encourages them to apply. Additionally, we seek to increase diversity of our workforce in areas where it is underrepresented and therefore explicitly encourage women and members of underrepresented groups to apply.