Msca - Dc13 - Phd Fellowship in Explainable Machine

**MSCA - DC13 - PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products.**:

- Réf **ABG-122120**
- Sujet de Thèse- 02/04/2024- Financement de l'Union européenne- University of Paderborn- Lieu de travail- Paderborn - Allemagne- Intitulé du sujet- MSCA - DC13 - PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products.- Champs scientifiques- Mathématiques
- Biotechnologie
- Informatique
- Mots clés- machine learning, serious game, plant-fermented food, computer science, applied mathematics**Description du sujet**:
Title: DC13, PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products - Development of a serious game to support the design of plant-based fermented food products.
Research field: Computer science, Applied mathematics.
Type of contract: Temporary.
Job status: Full-time.
Duration: 36 months.
Application deadline: 15/05/2024 23:59 - Europe/Brussels.
Envisaged job starting date: October 2024.

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 **Paderborn University** and the **French National Institute for Agriculture, Food, and Environment - INRAE** (PhD title delivered by the **Université Paris-Saclay**).

**Project description**:
**Scientific context**:
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. However, they are not always reusable because they are difficult to find and access and because databases are not systematically interoperable.

**Objectives**:
DC13 will collect, integrate and fuse knowledge about plant-based fermented food necessary to design, implement and evaluate a serious game on plant-based food synthesis. The knowledge to harmonise will cover several areas: technical (raw material used, microbial consortia, formulation, processing ), consumer (sensory and hedonic properties, expectations ), environmental (impact on climate change, resource use, ecosystems ) and more widely sustainability (e.g. stakeholder expectations for social aspects and prices for economic aspects). A representative case study will be chosen (e.g. plant-based fermented food products produced by DC1). Data will be collected from previous projects, scientific literature and relevant open databases (especially for technical data). The analysis of all the collected data will allow (i) the quantification of the causal relations between the different dimensions of the studied system and (ii) the identification of barriers and drivers to design the formulation and process of plant-based fermented food. This analysis will be formalised as a knowledge graph which serve as data to create a prototype of a serious game which supports the design of plant-based fermented food products, with a stakeholder approach.

**Expected results**:

- Formalised knowledge about plant-based fermented products including consumer, social and environmental data.
- The development of a prototype of a serious game to support the design of plant-based fermented food products.

**Location and planned secondment**:
The PhD student will be located **at Paderborn University**, Germany, for **20 months**. The secondment will be done **at INRAE (Joint Research Unit SayFood)**, Palaiseau, France, under the supervision of Dr. Caroline Pénicaud for a **16-month period**.

**Enrolment in doctoral degree**:
**Supervisors team**:

- **UPB team**: You will work with the Data Science group at Paderborn University. The group focuses on knowledge representation and machine learning at scale. It is particularly interested in models with rich semantics, e.g., RDF knowledge graphs. You will be supervised by **Prof. Dr. Axel Ngonga** (head of group) and **Dr. Mohamed Sherif** (head of the knowledge integration and fusion unit), both knowledge graph specialists.
- **INRAE team**:You will be based at the SayFood JRU (Paris-Saclay Food and Bioproduct Engineering Joint Research unit), which aims to contribute to product-process innovation by integrating upstream production constraints, consumer needs and expectations, and environmental issues. You will be supervised by** Caroline Pénica