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The Austrian Archaeological Institute (OeAI) of the Austrian Academy of Sciences (OeAW) is seeking for the interdisciplinary research project LEGION (machine LEarninG-enabled Identification of archaeological Objects in the middle daNube river basin) a

Student Research Assistant (f/m/x)

Starting in May 2026, the position is offered at 20 hours per week for a one-year term, with the possibility of extension.

LEGION focuses on the digital transformation of archaeological find processing at the intersection of Heritage Science and Artificial Intelligence (AI). Funded by the OeAW’s Heritage Science Austria 2.0 program, the project aims to develop an AI-supported system for the rapid and transparent automatic classification of ancient common ware from the Roman agglomeration of Carnuntum (Lower Austria/AUT), part of the UNESCO World Heritage site “Danube Limes.”

LEGION is conducted in close interdisciplinary cooperation between the OeAI/OeAW and the Computer Vision Lab (CVL) at the Technical University of Vienna (TU Wien). The core team is supported by strategic partners, including the State Collections of Lower Austria (LSNÖ), the Center for Museum Collections Management (ZMSW) at the University for Continuing Education Krems (UWK), the Roman City of Carnuntum, and the Austrian Centre for Digital Humanities (ACDH).

Utilizing a dataset of approximately 70,000 existing 2D profile drawings, LEGION will implement cutting-edge Machine Learning (ML) methods and continuous expert feedback (Human-in-the-Loop/HITL). By the project’s conclusion, it aims to provide a fundamental typochronology for ancient common ware in the Middle Danube region and present a low-threshold, open-source tool for rapid, transparent automatic identification and dating via eXplainable AI (XAI) for both legacy and newly created 2D drawings. Besides technological innovation, the project generates new insights into socioeconomic dynamics and settlement processes in Carnuntum by linking find data with spatial analyses. A further central focus is placed on responsible Research Data Management (RDM) and long-term archiving (LTA) according to FAIR (Findability, Accessibility, Interoperability, Reusability) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles, ensuring the sustainable availability of results for the international research community.

Your Tasks

The Student Assistant will support our interdisciplinary team in operational and data-centric tasks regarding RDM and LTA:

Your Profile

Our Offer

For content-related inquiries, please contact the PI at the OeAI, Dominik Hagmann: dominik.hagmann@oeaw.ac.at 

The Austrian Academy of Sciences (OeAW) pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity. Individuals from underrepresented groups are particularly encouraged to apply. 

Studentische:r Mitarbeiter:in (m/w/d)

Studentische:r Mitarbeiter:in (m/w/d)

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