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Dimitris Kotzinos

1 month ago

Postdoctoral Researcher (M/F): Distributed Machine Learning over Distributed Knowledge Graphs CY Cergy Paris Université in France

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Aug 30, 2026

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Country

France

University

CY Cergy Paris Université

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Keywords

Computer Science
Information Technology
Mathematics
Semantic Web
Database Management
Federated Learning
Cultural Heritage
Digital Twin Technology

About this position

Postdoctoral Researcher (M/F): Distributed Machine Learning over Distributed Knowledge Graphs at CY Cergy Paris Université, hosted in the ETIS Lab (UMR 8051) in Pontoise, France, in the Cergy-Pontoise area near Paris.

This postdoctoral position is part of ECHOES – European Cloud for Heritage OpEn Science, a Horizon Europe project (GA n° 101157364) contributing to the European Collaborative Cloud for Cultural Heritage (ECCCH). The project focuses on building a shared, distributed, and federated digital platform for cultural heritage professionals and researchers, with knowledge graph technologies at its core. The research environment combines machine learning, data management, and Semantic Web technologies, with strong links to European heritage infrastructures and open science initiatives.

The successful candidate will investigate distributed machine learning over distributed and federated knowledge graphs. Topics include federated graph representation learning, knowledge graph embeddings, relational graph neural networks, learning under statistical and semantic heterogeneity, ontology and schema alignment, entity resolution and entity alignment, link prediction, knowledge graph completion, communication-efficient optimisation, privacy-aware protocols, federated SPARQL query processing, distributed graph partitioning, and scalable analytics over evolving knowledge graphs. The work is expected to lead to novel scientific contributions as well as concrete components for the ECHOES ecosystem, with prototypes integrated into the ECCCH technical backbone and released as open source.

The position is supervised by Professor Dimitris Kotzinos and involves collaboration with European partners across the ECHOES consortium. The researcher is expected to publish in leading venues such as NeurIPS, ICML, ICLR, KDD, WWW, ISWC, ESWC, VLDB, and EDBT, contribute to project deliverables, and participate in the supervision of PhD and MSc students working on related topics.

Eligibility and profile: a PhD in Computer Science or a closely related field is required before the start date. Strong experience in at least one of graph machine learning, federated/distributed learning, knowledge graphs and Semantic Web technologies, or distributed data management is expected. Applicants should have solid Python programming skills and familiarity with PyTorch, PyTorch Geometric, DGL, or similar tools; experience with triple stores or graph databases is an advantage. A record of publications in recognised international venues is required, along with strong English skills. French is not required.

Funding and contract: the appointment is a 12-month temporary full-time postdoctoral contract, extensible, with salary according to the CY Cergy Paris Université salary scale and commensurate with experience. The position is funded through Horizon Europe as part of the ECHOES project.

Application deadline: 30 August 2026 at 23:59 (Europe/Paris). Applications are reviewed on a rolling basis and the position starts as soon as possible.

How to apply: send an e-mail application to [email protected] with the subject line “ECHOES Postdoc Application – [Your Name]”. Include a detailed CV with publications, a cover letter, contact details of two or three referees, and a copy of your PhD certificate or expected defence date. Up to three representative publications may also be included.

Funding details

Available

What's required

PhD in Computer Science or a closely related field must be obtained before the starting date. Strong background in at least one of the following is required: machine learning on graphs (GNNs, knowledge graph embeddings), federated/distributed learning, knowledge graphs and Semantic Web technologies (RDF, SPARQL, OWL, CIDOC-CRM is a plus), or distributed data management. Candidates should have solid programming skills in Python and tools such as PyTorch, PyTorch Geometric, or DGL; experience with triple stores or graph databases is appreciated. A track record of publications in recognised international venues is expected. Applicants should be able to work in a multidisciplinary international consortium, and excellent written and spoken English is required; French is not required.

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