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Early Stage Researcher (ESR13)
PhD fellowship in Fermentation and flavour development of plant-based cheese analogues using different starter cultures
“E-MUSE Complex microbial ecosystems multiscale modelling: mechanistic and data driven approaches integration” MSCA-ITN-2020 European Training Network
NIZO FOOD RESEARCH BV (NIZO),
KERNHEMSEWEG 2, EDE GLD 6718 ZB, The Netherlands
Food microbiology (dairy and dairy alternatives), fermentation and safety, data processing (phenotypes, genotypes), predictive modelling
1st March 2021 00:00 - Europe/Brussels
Envisaged Job Starting Date
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.
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.
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) (https://www.wur.nl/en.htm)
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
• networking and good communications skills (writing and presentation skills)
• willingness to travel abroad for the purpose of research, training and dissemination.
English: B2, good oral and written communication skills in English are compulsory
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
NIZO (www.nizo.com) is a leading company in contract research for better food and health with one of the most advanced R&D centres in the world. NIZO has state-of-the-art research facilities, highly skilled experts and comprehensive expertise on bacteria, proteins and processing. NIZO’s focus is on the development and application of innovations for the global food industry and related markets. This includes cheese research, development and production, and development of plant-based alternatives. With 70 years’ experience with starter cultures and food safety and quality control and with up-to-date new technologies including genomics approaches, NIZO brings the latest food technologies to life. NIZO’s customers value their gains in product quality, sustainability, cost effectiveness and speed to market. NIZO is continuously looking for new ways of improving food products, and at the same time quality of life.