About us:

The ERC-funded project “Rewriting the End of Cuneiform Civilization” (RECC) aims to revolutionize our understanding of the final centuries of cuneiform culture through cutting-edge digital humanities and machine learning techniques. The project, based on the previous work of the group (see here and here) will develop tools for paleographic analysis, automatic dating of cuneiform tablets, and the study of scribal practices in Hellenistic and Parthian Babylonia.

We are looking for you:

Student Assistants in AI for Science / Machine Learning at the ERC Project: “Rewriting the End of Cuneiform Civilization” (m/f/x)

in Munich

Your tasks and responsibilities:

• Assist with the development and implement the Signarium platform, a comprehensive paleographic database for cuneiform signs.
• Apply Machine Learning techniques (e.g., CNNs, PCA, generative AI) to analyze and classify cuneiform signs, enabling automatic dating of tablets.
• Collaborate with the Machine Learning Consulting Unit (MLCU) and interdisciplinary teams to refine models and ensure interpretability of results.

Your qualifications:

• BA or MA student in a relevant field (Statistics, Machine Learning, Data Science, Computer Science, etc.)
• Experience in image segmentation, deep learning, and feature extraction (e.g., PyTorch, TensorFlow).
• Proficiency in Python and collaborative tools (Git/GitHub).
• Fluency in English.

Benefits:

• The opportunity to be part of a dynamic, internationally connected team working at the forefront of several disciplines.
• Career development through the LMU qualification program.
• A stimulating working environment at one of Germany’s top-ranked universities. Our institute is located in the heart of the vibrant and internationally diverse city of Munich with excellent public transportation links.
• Collaboration with Assyriologists, ML experts, and international partners.
• Affiliation with the Munich Center for Machine Learning (MCML), one of the leading AI centers in Germany and worldwide.

People with disabilities who are equally as qualified as other applicants will receive preferential treatment.

Contact:

Submit your CV, transcripts, motivation letter (one page), and one code sample (e.g., GitHub link) as a single PDF to: sekretariat.jimenez@assyr.fak12.uni-muenchen.de

Subject: Application for Student Assistant 2 (RECC Project)

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