Job description

Title: DC9, PhD fellowship in the design of Information Extraction Tools to characterise molecules produced or degraded by microbes and applications to plant-fermented food ecosystems.
Researcher profile: Doctoral candidate.
Research field: Artificial Intelligence, Natural Language Processing and Bioinformatics.
Type of contract: Temporary.
Job status: Full-time.
Duration: 36 months.
Application deadline: CLOSED.
Envisaged job starting date: October 2024.
How to apply:  It is not possible to apply to this project anymore.

Hiring organisation and offer posting contact details

Organisation: INRAE Jouy-en-Josas, UR1404 Applied Mathematics and Informatics from Genome to the Environment (MaIAGE).
Number of positions available: 1
Country: France.
Address: 78352 Rue de la Manufacture, 78350 Jouy-en-Josas.
Contact email: fairomics-dc10@inrae.fr

Please note that this PhD position will lead to the award of a double diploma after the completion of a stay in each of these organisations: The University of Paris-Saclay (UPSaclay), France and the University of Szeged (USZ), Hungary.

Offer description

In brief:

We are looking for one Doctoral Candidate (DC) to join our project at multiple sites in the EU with a master’s degree in a relevant discipline (Master’s degree in engineering, physics, systems biology, applied mathematics, biotechnology) interested in Modelling, analysis and control of biological systems in the context of microbial fermentations.

FAIROmics project:

The FAIROmics initiative, an interdisciplinary research programme, will gather universities, research centres and private companies to enable the FAIRification of omics data and databases interoperability and develop knowledge graphs for data-driven decision-making to rationally design microbial communities for imparting desirable characteristics to plant-based fermented foods in the context of open science and its regulations. The FAIROmics training programme aims to develop doctoral candidates’ skills at the interface between artificial intelligence, life sciences, humanities, and social sciences.

Scientific context:

Plant-based fermented food products have become popular because of sustainability, health benefits, lifestyle trends, and dietary restrictions. The knowledge of food properties and microbe habitats, phenotypes, and metabolic pathways is critical for developing new nutritionally balanced and flavorful food products. This knowledge is spread in the scientific literature and free text fields of databases, thus requiring dedicated Natural Language Processing Ontology-based tools.

Objectives:

The PhD project aims to develop information extraction (IE) methods to automatically produce a knowledge graph about microbe biology involved in plant-based food transformation or preservation. The knowledge graph will formalise the molecules produced and degraded by microorganisms in the fermentation process.

The IE methods will involve named-entity recognition, entity normalisation with respect to semantic references and relationship extraction. They will be based on the most recent deep learning approaches that train language models using few or no training examples by transfer learning or exploiting existing structured information, i.e. knowledge bases and ontologies for distant or weak learning by including relevant information according to the needs of the FAIROmics dedicated use cases (e.g. NCBI Taxonomy for taxa, FoodEX2 for food, ChEBI for molecules, KEGG for pathways). Existing annotated corpora will serve as a starting point for training (e.g. CHEMDNER, Pathway Curation, Bacteria Biotope).

The project will rely on existing tools and resources on microbe biology developed by MaIAGE partners (e.g. Omnicrobe application*, Ontobiotope ontology*, extraction workflow).

Expected results:

The PhD student will design and evaluate original machine-learning-based methods for extracting information on plant-based fermentation metabolism from text. The models and software will be available to the scientific community in an open-source license. The extracted knowledge will feed a publicly available knowledge graph of microbial properties. The results will be published in the major NLP venue and relevant bioinformatics journals.

Enrolment in Doctoral degree:

1st-degree awarding organisation: University Paris-Saclay, https://www.universite-paris-saclay.fr/
2nd degree awarding organisation: University of Szeged, https://u-szeged.hu/english

Location and secondment:

The PhD student will be mainly located at the INRAE site in Jouy-en-Josas for 24 months and at the Szeged University for a 12-month secondment.

Required skills/qualifications

- Master's degree or equivalent in AI, NLP and ML.
- Strong background in AI and NLP acquired at the Master's level. Significant work experience or training in biology is a plus.
- Solid computer development skills.
- Applicants must demonstrate an openness to learn new things, versatility, creativity, problem-solving skills, and attention to detail.
- Networking and communication skills in a multicultural and multidisciplinary environment.
- Willingness to travel abroad for the purpose of research, training and dissemination.

Eligibility criteria

- Any nationality
- Doctoral Candidate (DC): The applicant must not have been awarded a doctoral degree.
- Mobility rule: The DC must not have resided or carried out main activity (work, studies, etc.) in the country of their host organisation for more than 12 months* in the 3 years immediately prior to the date of selection in the same appointing international organisation.
* EXCLUDED: short stays such as holidays, compulsory national services such as mandatory military service and procedures for obtaining refugee status under the General Convention.
- Language: Applicants must demonstrate fluent reading, writing and speaking abilities in English (B2).

Supervision team

French team:
Two MaIAGE teams will be involved in the PhD supervision: the Bibliome team* and the StatInfOmics team* :
- Robert Bossy (Bibliome): Natural Language Processing and application to microbiology, software engineering.
- Claire Nédellec (Bibliome): Natural Language Processing and application to microbiology, knowledge representation and ontology.
- Hélène Chiapello (StatInfOmics): Microbial bioinformatics, omics data.
- Sandra Dérozier (StatInfOmics): Microbial bioinformatics, software engineering.

Hungarian team :
- Vidács Lázló: Artificial intelligence, natural language processing, software engineering.
- Balázs Nagy: Artificial intelligence, natural language processing, software engineering.

Hosts institutions description

INRAE is Europe’s top agricultural research institute and the world’s number two centre for the agricultural sciences. Its scientists are working towards solutions for society’s major challenges. RU1404 MaIAGE gathers mathematicians, computer scientists, bioinformaticians and biologists to tackle problems from biology, agronomy and ecology. Our research concerns processes at various levels, ranging from molecular, cellular or multicellular levels to organisms, populations, and entire ecosystems.

The University of Szeged (USZ) is recognised as a top research institution in Hungary, boasting a diverse student body of over 21,000, including more than 4,000 international students from 115 countries. Led by László Vidács, the Applied Artificial Intelligence Research Group is dedicated to advancing cutting-edge AI research. We specialise in diverse AI applications, from natural language understanding to image processing. Our tailored machine learning and deep learning solutions address real-world challenges in many domains, including medical imaging diagnostics, forensic text analysis, and program source code processing.

We offer

- A comprehensive, interactive and international training programme covering the broader aspects and interface between life science, data science, artificial intelligence and humanities and social sciences, as well as transferable skills.
- An enthusiastic team of professionals to co-operate with.
- Personal Career Development Plan (PDCP) to prepare young researchers for their future careers.
- Each DC will undergo individual training at individual institutes according to the PCDP description.
- An attractive compensation package in accordance with the MSCA-DN programme regulations for doctoral candidates. The exact salary will be confirmed and will be based on a living allowance of 3400€/month (correction factor to be applied per country) + mobility allowance of 600€/month. Additionally, researchers may also qualify for a family allowance* of 660€/month, depending on the family situation. Taxation and social (including pension) contribution deductions based on national and company regulations will apply. 
*family = be married/be in a relationship with equivalent status to a marriage recognised by the legislation of the country or region where it was formalised/have dependent children who are being maintained by the researcher.

Selection process

1. Candidates apply for a position using the online application form.
2. The FAIROmics Project Manager provides a first screen of the written applications to check the eligibility of the candidate and forwards the eligible applications to the DC supervisors.
3. The DC supervisors will select the best candidates based on CV¹, cover letter², academic records, recommendation and motivation letters and adequate skill set. To better assess the best candidate, the shortlisted candidates might be asked to write an abstract of provided scientific documents relevant to the research subject.
4. The selected applicants will be interviewed through an online meeting by the Selection Committee (two main supervisors and two representatives of a beneficiary or associated partner, with at least one person external to the DC’s project).
5. The best candidates will be chosen by the main supervisors. The European Project Manager will communicate the successful candidates to the Consortium and Partners.

¹: A curriculum vitae listing degrees awarded, courses covered and marks obtained, publications, relevant experience and names of two referees who could be contacted for reference is expected.

²: Write a cover letter with a statement of research interests, outlining why you are interested in this PhD position/topic, how you plan to approach the research task, and why you consider your experience relevant.