Julie Bourbeillon is seeking a fellow for MSCA PF 2026 in bio data mining at L’Institut Agro, Angers, France

Organisation / Company Institut Agro Rennes-Angers Is the Hosting related to staff position within a Research Infrastructure? No

Offer Deadline: Fri, 31 May 2026 - 17:00

Country: France

City: 49045 ANGERS Cedex

Organisation/Institute

Organisation / Company: Institut Agro

Department: Applied mathematics and Computer Sciences

Laboratory: Institute of Research in Horticulture and Seeds (IRHS), ImHorPhen team

Is the Hosting related to staff position within a Research Infrastructure? No

Institut Agro (AGRO) is France's largest Engineering School focused on food, agriculture, and the environment. Founded in 2020, AGRO resulted from the merger of three prestigious and long-established schools: Agro Rennes-Angers, Dijon, and Montpellier. It is designed to provide France with a leading, university-level institution focusing on food, agriculture and the environment. It has three main aims: to train students, conduct academic and applied research and transfer knowledge to society at large.

AGRO delivers post-graduate education to 5 000 students in total, among which 450 PhD candidates and 2 500 students of masters of engineering navigating 8 tracks and 30 majors. AGRO is internationally recognized as a major player in capacity development in agrifood systems engineering, design and management. Its expertise encompasses life science, food science, agriculture, livestock and fish, horticulture and viticulture, agri-business, rural development, ecosystems, biodiversity, landscape and natural resource management, socio-economy and policies, bioeconomy and climate change. It employs 300 faculty members collaborating with 1250 researchers in its 39 research units in partnership with INRAE, CIRAD, CNRS, IRD, IFREMER and INSERM.

In Angers, AGRO jointly supervises the Institute of Research in Horticulture and Seeds (IRHS) with INRAE and the University of Angers. The IRHS gathers the main regional research actors in Plant Sciences. With currently more than 250 agents, including 183 permanent staff, it integrates expertise in genetics, genomics and epigenomics, physiology and ecophysiology, biochemistry, plant pathology, microbiology, modeling, bioinformatics, biostatistics and biophysics for the quality and health of horticultural species and seed production.

As part of the IRHS, the ImHorPhen (Imaging for Horticulture and Phenotyping) team research topics focus on low cost computer vision and machine learning, simulation assisted plant phenotyping and machine learning based data mining for plant biology.

Description of the project

My research mainly focuses on applying semantic web techniques to develop methods and tools that streamline the acquisition, management, integration, and interpretation of biological data, with a special emphasis on plant biology. I address a range of challenges, such as handling heterogeneous datasets—often mixing qualitative and quantitative variables—optimizing dimensionality reduction, and designing effective ways to visualize complex results. Central to my work is leveraging semantic web techniques, and in particular ontologies, to represent real-world biological knowledge in order to enhance data consistency and analytical robustness.

Among recent works is the introduction of a novel semantic distance metric that accurately encompasses both quantitative and qualitative variables, enabling a richer depiction of relationships between individuals (e.g., phenotypic descriptions). This semantic distance is integrated into a complete tool that takes biologists from raw data input all the way to intuitive visualization, thereby simplifying the adoption of advanced semantic approaches and enhancing the overall efficiency and clarity of plant biology research.

Another contribution is a pipeline tailored to describe a factor (a qualitative variable of interest) in heterogeneous datasets consisting of qualitative and quantitative variables. This pipeline separately analyses the variables related to the factor using appropriate statistical tests and offers an interactive visualisation that describes the factor.

As part of the MSCA proposal II would like to focus on some of the problems posed by accessing large, heterogeneous plant datasets:

1. Identifying individuals: helping taxonomists resolve ambiguities in the naming of taxa between different reference systems, or even in the absence of a reference system.
2. Consolidating vocabulary: proposing an automatable method for reconciling the terms used in different datasets to describe measurements and modalities of qualitative variables.
3. Integrating data: jointly processing data from different scales (spatial, temporal or related to the experimental context) by matching similar individuals and variables, while taking into account differences in context through appropriate mathematical or statistical transformations.

I hypothesise that the combination of ontologies, repositories, and artificial intelligence methods applied to the underlying problems (ontology alignment, automatic language processing, etc.) can help answer these questions and assist biologists in better understanding datasets.

In our team, we view computer science and data mining as essential tools for assisting biologists in characterizing plants and gaining new insights from their data. We particularly enjoy discovering innovative methods and developing new concepts.

AGRO and other local research institutions organise a Springboard toward Marie Sklodowska Curie programme. Selected candidate researchers will have the opportunity to attend online intensive training and coaching course on how to write a successful proposal. This masterclass is a pathway of support to candidates and their supervisors in preparing project proposal, including prescreening activity of the draft proposals and the organisation of dedicated B2B meetings, by the European Research Office of University of Angers. More information here:

Keywords

Computer Science – Semantic Web – Ontologies - Data mining - Data Integration - Dimensionality reduction – Data Visualisation – Plant Biology

Eligibility

Applicants must comply with the mobility rule (having stay in France less than 12 months in the past 3 years before the 09th of September 2026). Applicants also must have maximum 8 years of full-time research experience after graduating their (first) PhD.