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Early Stage Researcher (ESR13)  

PhD fellowship in Fermentation and flavour development of plant-based cheese analogues using different starter cultures

Project Title

“E-MUSE Complex microbial ecosystems multiscale modelling: mechanistic and data driven approaches integration” MSCA-ITN-2020 European Training Network

Hosting Organization


KERNHEMSEWEG 2, EDE GLD 6718 ZB, The Netherlands

Researcher Profiles


Research Field

Food microbiology (dairy and dairy alternatives), fermentation and safety, data processing (phenotypes, genotypes), predictive modelling

Application Deadline

1st March 2021 00:00 - Europe/Brussels

Envisaged Job Starting Date



3 years

Contract Type

full-time employment (based on COVID-19 evolution and restrictions, possibility to start remotely, once situation allows the presence is required)


€3 528.33 gross salary/month

+ €600 gross mobility allowance/month and €500 gross family allowance/month (if applicable)

Taxation and Social (including Pension) Contribution deductions based on National and company regulations will apply.


A. Set-up and execution of plant-based fermentations: Upon selection and preparation of plant based ingredients, large numbers of lactic acid bacteria (with known genome sequences) will be used to perform fermentations using in vitro high throughput methodologies. Relevant phenotypic data and fermentation characteristics will be analysed, namely: Acidification, Proteolysis, Relevant enzymatic activities, Flavour compounds (GC-MS). B. Identification of favourable fermentation outcomes by data analysis of the large datasets (e.g. ingredients, strains, fermentation outcomes): By mapping phenotypic traits and fermentation outcomes of the different strains (e.g. flavour development, acidification rates impacting safety) on different substrates versus the genome content (gene-trait matching), clustering of well-performing strains for different applications will be performed. C. Assess the impact of the fermentation process and storage/ripening with respect to potential spoilage and safety: For well-performing plant-protein / strain combinations, control of sporeformers spoilers and pathogen during fermentation and shelf life will be assessed. D. Integrate data for predictive modelling approaches: In collaboration with other early stage researchers in the EU project, deep learning methods will be developed to predict strain- and condition-dependent properties to produce different plant-based cheeses.

Expected Results

Predictive tool that allows for rational selection of strains and fermentation conditions to produce high quality cheese analogues based on plant-based proteins, based on basic genomic and phenotypic data of starter culture strains.

Planned Secondments

In total, 4 months will be spent at the University of Szeged in Hungary (machine learning) and 2 months at the KU Leuven in Belgium (multiscale modelling) as part of the training network activities.

Enrolment in Doctoral degree

Wageningen University (WU) (


Required Education Level

Master’s degree in Food Science with demonstrable affinity for microbiology, with a strong background in data science and mathematical modelling.

Skills / Qualifications

•    hands-on experience with standard microbiological techniques, enzymatic assays and chemical analytical techniques
•    strong background in data processing and interpretation
•    experience with beneficial and pathogenic food bacteria is favored
•    affinity with genome based data analyses is favored
•    experience in reporting
•    a proactive attitude with a strong sense of responsibility
•    an open communication style with attention for the team
•    quality-driven
•    networking and good communications skills (writing and presentation skills)
•    willingness to travel abroad for the purpose of research, training and dissemination.

Specific Requirements

For the eligibility please check: Eligibility Criteria

Required Languages

English: B2, good oral and written communication skills in English are compulsory

Supervisors Team

Within NIZO, the Unit of Food Safety and Fermentation, in which the proposed work will be carried out, has a vast knowledge on fermentation characteristics of starter culture strains when grown on dairy and plant substrates in relation to development of flavour and controlling food safety and quality.

The lead supervisor is M. Wells-Bennik who works as Principal Scientist at NIZO. She has a background in food microbiology (including functional genomics expertise) and is a visiting scientist at Wageningen University, with experience with PhD student supervision. Co-supervisors at NIZO have extensive expertise in fermentation, dairy and dairy-alternative technology,  bioinformatics (genome analysis), and predictive modelling. Co-supervisors at Wageningen University have a strong background in fermentation and predictive modelling.

Host Institution Description

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