About Position/Job
The Institute for Digital Cultural Heritage Studies (IDCHS) at Ludwig-Maximilians-Universität München (LMU Munich) is inviting applications for a fully funded Postdoctoral Researcher position focused on Artificial Intelligence (AI) and Machine Learning (ML) applications in Heritage Science and Archaeology. This exciting role supports LMU’s commitment to driving transformative research that combines digital innovation with cultural heritage studies. The successful candidate will join a dynamic, internationally connected team and engage in groundbreaking national and international projects. This position offers an exceptional opportunity to shape the future of digital cultural heritage, contribute to teaching, and develop interdisciplinary methods that address contemporary societal challenges. The initial contract is for three years, with the possibility of renewal for an additional three years after a positive evaluation.
Position/Job Details
- Position: Postdoctoral Researcher (Akademischer Rat auf Zeit)
- Institution: Ludwig-Maximilians-Universität München
- Department: Institute for Digital Cultural Heritage Studies (IDCHS)
- Research field: AI, Machine Learning, Heritage Science, Archaeology
- Location: Munich, Germany
- Number of posts: 1
- Job Type: Full-time, postdoctoral, temporary (3+3 years)
- Start Date (Anticipated): October 2025
- Working Hours: 40 hours per week
- Salary Range: Public service scale A13 (Bavaria)
- Required Degree/Diploma: PhD
Research area
- AI and Machine Learning for cultural heritage applications
- Automated analysis of archaeological and heritage datasets
- Remote sensing and proximal sensing techniques for heritage science
- AI-driven visualisation and virtual reconstruction of sites and artefacts
- Multimodal large language models for cultural collections and archives
Eligibility & Qualifications
Minimum Requirements:
- PhD (completed by 1 October 2025) in Computer Science, Data Science, Statistics, Digital Heritage, Archaeology, or a related field with a focus on AI/ML.
- Proven experience in AI/ML applications, particularly in cultural or archaeological contexts.
- Excellent English communication skills (written and spoken).
- Strong proficiency in Python and collaborative coding tools (e.g., GitHub).
Preferred Qualifications:
- Experience with deep learning, especially computer vision and 3D data.
- Familiarity with frameworks such as PyTorch, TensorFlow, or JAX.
- Background in teaching at university level and supervising students.
- Experience in grant writing and interdisciplinary collaboration.
- Interest in open science and ethical AI applications.
Key Responsibilities
- Develop and lead innovative interdisciplinary research projects combining AI/ML with heritage science.
- Publish in high-impact journals and present at leading conferences.
- Collaborate with the Machine Learning Consulting Unit (MLCU) and other international partners.
- Teach Master’s-level courses in AI/ML and data science (five hours per week in English).
- Supervise research assistants and Master’s theses.
- Support administrative tasks at IDCHS, including lab management and student admissions.
- Contribute to grant applications and future research funding strategies.
Application Process
How to Apply:
Submit your complete application as a single PDF (up to 15 MB) via email to jobs-research@dkes.fak12.lmu.de.
Required Documents:
- Cover letter
- Curriculum vitae
- Research statement (max. 3 pages)
- Teaching statement (max. 2 pages)
- Names and contact details of three academic referees
- Proof of academic degrees
- Up to five writing/code samples (e.g., publications, GitHub link, syllabi, funded proposals)
- Link to digital portfolio (if applicable)
Important Dates
- Application Opens: 8 July 2025
- Deadline: 27 July 2025 (23:59, Europe/Berlin)
- Interview Date: To be announced
Useful Links
- Institution Website: LMU Munich IDCHS
- Official Notification Link: View Official Call
- Apply now: submit via email to jobs-research@dkes.fak12.lmu.de
Stay updated on the latest academic jobs: HigherJobz
