Post-Doctoral Research Visit F/M Neurosymbolic Relational Graph Neural Networks

Inria; France; Hybrid; EUR 40,000 - 50,000; Full time

Benefits offered by this job

Transport reimbursement

Teleworking after 6 months

Flexible hours

Annual leave 7 weeks + RTT days

Professional equipment

Social & cultural events

Job summary

Inria, France's digital science and technology research institute, invites applications for a Post-Doctoral Research Visit in the TYREX team. The project investigates neurosymbolic relational GNNs by injecting symbolic knowledge into message passing, guiding schemas, constraints, and rules for more structured learning.

The role emphasizes developing GraphSAGE-style extensions and atomic-route-based approaches, building a benchmark, and providing empirical evidence on improvements in accuracy,

Qualifications

• PhD or equivalent required.

Responsibilities

• Characterize how symbolic knowledge can be represented and injected into relational GNNs.
• Develop neurosymbolic extensions of GraphSAGE-style aggregation for relational databases.
• Develop symbolic and differentiable variants of atomic-route-based message passing.
• Contribute to building a benchmark for neurosymbolic relational learning (datasets, annotations, tasks, evaluation).
• Provide empirical evidence on when symbolic knowledge improves relational learning (accuracy, robustness, data efficiency, interpretability).

Education

PhD or equivalent

Job description

Post-Doctoral Research Visit F/M Neurosymbolic Relational Graph Neural Networks

Level of qualifications required : PhD or equivalent

Fonction : Post-Doctoral Research Visit

Context

Within the TYREX research team : tyrex.inria.fr

Assignment

Relational learning has recently gained renewed momentum through the development of end-to-end deep learning approaches over relational databases. In this setting, relational tables are typically transformed into heterogeneous graphs, where rows become nodes and primary–foreign key relationships induce edges. GNNs can then propagate information across the relational structure and learn predictive representations without relying exclusively on manual feature engineering. Benchmarks such as RelBench have made this research direction more concrete by providing realistic relational datasets and prediction tasks, while models such as Rel-GNN have shown that relational database structure can be exploited more effectively through carefully designed message passing mechanisms.

Despite this progress, current relational GNNs remain largely statistical. They exploit graph connectivity and learned node or edge representations, but they make limited use of the symbolic and semantic information that is naturally available in relational databases. Such information includes schemas, entity and attribute types, integrity constraints, functional dependencies, temporal constraints, business rules, and domain-specific expert knowledge. As a result, existing models may require large amounts of labelled data, may generalize poorly outside the training distribution, and often provide limited interpretability regarding the relational paths used to produce a prediction.

The central hypothesis of this postdoctoral project is that symbolic knowledge can be introduced into relational GNNs in a scalable and effective way by acting on the message passing structure itself. Instead of treating symbolic knowledge as a separate reasoning layer added after learning, the project will investigate how schemas, constraints, and rules can guide the construction, selection, weighting, and interpretation of relational message passing routes. This is particularly relevant for GraphSAGE-style architectures, which rely on local neighborhood aggregation, and for Rel-GNN-style architectures, which introduce atomic routes as elementary paths enabling more direct and structured information exchange between heterogeneous relational entities.

Main activities

The expected contributions of the postdoctoral project are fivefold. First, the project will provide a principled characterization of how symbolic knowledge can be represented and injected into relational GNNs. Second, it will introduce neurosymbolic extensions of GraphSAGE-style aggregation for relational databases. Third, it will develop symbolic and differentiable variants of atomic-route-based message passing inspired by Rel-GNN. Fourth, it will contribute to the construction of a benchmark for neurosymbolic relational learning, including datasets, symbolic annotations, tasks, and evaluation protocols. Fifth, it will deliver empirical evidence showing when and how symbolic knowledge improves relational learning in terms of accuracy, robustness, data efficiency, constraint satisfaction, and interpretability. Results will be published in first rank venues in AI, databases.

Benefits package

• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Theme/Domain :Data and Knowledge Representation and Processing
  Information system(BAP E)

Defence Security :
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

Recruitment Policy :
As part of its diversity policy, all Inria positions are accessible to people with disabilities.

Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.