FAIROmics -PhD fellowship in Network-based algorithms for Knowledge Graph construction and analysis and application to the Omnicrobe knowledge graph.

About the Project

Plant-based dairy and meat alternatives have grown in popularity in recent years for various reasons, including sustainability and health benefits, as well as lifestyle trends and dietary restrictions. However, plant-based food products can be nutritionally unbalanced, and their flavour profiles may limit their acceptance by consumers. Microorganisms have been used in making food products for millennia. However, the diversity of microbial communities driving plant-based fermentations, as well as their key genetic and phenotypic traits and potential synergies among community members, remain poorly characterised. Many data exist, but they are spread into different literature (scientific and grey) or, in the best case, in different databases.

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.

Application deadline: 15/05/2024 23:59 – Europe/Brussels.

Envisaged job starting date: October 2024.

Hosting organisation: Department of Physics and Astronomy, Bologna University, Italy.

Planned secondment: One in INRAE MaIAGE, France for a duration of 12 months.

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: University of Bologna (UNIBO), Italy and the University of Paris-Saclay (UPSaclay), France.

Offer description:

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 interested in learning and developing Network and Machine Learning tools for the analysis and construction of Knowledge Graphs related to bacterial communities used for food processing.

To understand the network structure of Knowledge Graphs to develop and test algorithms for their characterisation and optimisation. In particular, spectral approaches based on the network Laplacian operator and techniques for node embedding derived from AI (eg DeepWalk, node2vec, Transformer Networks or Autoencoders) will be tested, providing an interpretation of KG elements useful for manifold learning or geometric deep learning. Moreover, network analysis theory, such as community structure characterisation or identification of key elements (nodes, links, pathways), will be studied and applied to the available cases.

In the FAIROmics project framework, the nodes of the graph represent biological entities, e.g. bacteria, food matrix, food ingredient, metabolites, genes, the function of these genes, etc. The edges represent relationships between these entities, e.g. bacteria growing in the food matrix. Moreover, the entities are themselves linked to reference classes, defined in knowledge graphs such as ontologies (e.g. bacteria taxa in NCBI taxonomy, food matrix in FoodEx2).

Specific data will be analysed and produced within the FAIRomics project, and as a starting case study the Omnicrobe knowledge graph, containing bacteria habitats and phenotypes, will be analysed to characterise element similarity at different levels (nodes, paths, modules, communities), allowing to check network structure, possible inconsistencies, missing or hidden relationships.

It is expected to get a deeper comprehension of Knowledge Graph structures and how to query and manipulate them. This should allow them to improve their understanding and usability, for example by:

1. identifying hidden relationships through link imputation and analysis of node embedding similarity;
2. extracting possible relevant outliers or anomalies (regarding ontologies/nodes and/or relationships/links) corresponding to wrong elements within KGs;
3. clustering of KG elements through network community algorithms;
4. identify “knowledge modules” through network diffusion algorithms. The developed Network tools are also useful in a wide range of contexts, from biological networks (e.g. Protein interaction, Gene regulation, Microbial community ecology) to Social networks (structure and dynamics of social networks, sentiment analysis, node classification, etc.).

Required skills/qualification:

• Master’s degree in Physics, Computer science, Mathematics, Statistics, Engineering or related fields.
• Good skills in programming high-level languages like Python, R, and Matlab (not mandatory but highly recommended).

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.

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 three years immediately prior to the date of selection in the same appointing international organisation.
• Language: Applicants must demonstrate fluent reading, writing and speaking abilities in English (B2).

Recruitment process:

1. Candidates apply for a position using the online application form (ACCESSIBLE HERE).
2. The Project Manager provides a first screen of the written applications to check the eligibility of the candidate.
3. The DC supervisors will select the best candidates based on CV, academic records, recommendation and motivation letters and adequate skill set.
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.

For more information about the offer, the project and the recruitment process, please visit our website.