Location: KU Leuven, Gent, Belgium
Chemical & Biochemical Process Technology & Control (BioTec+), Ghent and Aalst Technology Campuses
Employment percentage: Full-time
Project
H2020-MSCA-ITN E-MUSE: Complex microbial Ecosystems MUltiScale modElling: mechanistic and data driven approaches integration
https://www.itn-emuse.com/
The E-MUSE training programme aims at developing young researchers’ skills at the interface between artificial intelligence and life sciences. The challenge is to acquire a shared language bridging life science questions and original modelling approaches. The research programme of the E-MUSE network is to develop innovative modelling methodologies to understand a complex microbial ecosystem and identify levers to control and/or predict its evolution. To deal with biological complexity, biologists, mathematicians, and computer scientists have to work together to develop innovative methodologies. An important complexity of this domain originates from scales and dynamics issues, ranging from local kinetics at the level of the cell to emerging macroscopic properties of the biological system. The development of high throughput techniques provides more and more large datasets, but knowledge is not easily inferred from this huge amount of data and multiscale dynamics are still incompletely characterised and predicted. E-MUSE’s transdisciplinary network gathers academic and industrial partners to equip (15) Early Stage Researchers (ESRs) with scientific, research and transferable skills to become leaders in academic research or industry. They will be at the cutting edge of the modelling methodologies that we apply to model structural and dynamic features of microbial communities, to identify key processes and biomarkers for specific applications.
ESR14 to be recruited by KU Leuven/BioTeC+ will set-up and execute fermentations with cheeses (plant-based or hybrid) and study the effect of various storage/ripening conditions on flavour development and microbial stability. Based on the data obtained, ESR14 will develop a mathematical model to predict condition-dependent characteristics of plant-based cheeses. Correlations between ripening conditions, cheese properties and the final flavour of the product will be investigated using linear and non-linear methods. Based on the identified relations and the data available, a regression model based on machine learning methods will be trained and validated for the prediction of the properties of cheese analogues.
Expected result:
This project is expected to generate a mathematical in silico model that predicts the impact of ripening conditions of semi hard cheese on flavour formation which will be extended to plant based cheese analogues based on high throughput experimental data (through in vitro cheese models) obtained in the project. The development of plant-based cheese analogues (in vitro cheese models) will set new standards for generating reproducible data sets.
Profile
We are seeking highly motivated candidates that hold a master in Food Science/Food Technology/Biological Engineering/...
- with demonstrable affinity for microbiology, with a strong background in data science and mathematical modelling,
- hands-on experience with standard microbiological techniques and chemical analytical techniques,
- strong background in data processing and interpretation,
- experience with beneficial and pathogenic food bacteria is favored.
The candidate should be open to interdisciplinary collaboration and be interested in both fundamental and applied research.
Specific eligibility criteria for H2020-MSCA-ITN networks:
- Early-stage researchers (ESRs) are those who are, at the time of recruitment by the host, in the first four years (full-time equivalent) of their research careers and have not been awarded a doctoral degree. This is measured from the date when they obtained the degree which formally entitles them to embark on a doctorate, either in the country in which the degree was obtained or in the country in which the research training is provided, irrespective of whether or not a doctorate was envisaged. The applicants cannot already hold a PhD.
- Researchers are required to undertake trans-national mobility (i.e., move from one country to another) when taking up the appointment. At the time of selection by the host organisation, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organisation for more than 12 months in the 3 years immediately prior to their recruitment. Short stays, such as holidays, are not taken into account.
- ITN fellows (ESRs) must demonstrate that their ability to understand and express themselves in both written and spoken English is sufficiently high for them to derive the full benefit from the network training. Non-native English speakers are required to provide evidence of English language competency before the appointment is made.
Offer
- 3 years financial support to pursue a PhD (including Living, Mobility and Family Allowance according to H2020-MSCA-ITN regulations);
- The H2020-MSCA-ITN E-MUSE training network of 15 PhD students offers a unique environment for international academic-industrial collaboration;
- Being part of the dynamic KU Leuven/BioTeC+ research group (6 postdocs and about 14 PhD researchers), headed by Prof. Jan F.M. Van Impe;
- Long term secondment at NIZO Ede, The Netherlands for experimental data generation;
- Additional secondment of 2 months at INRAe Paris, France.
More info: https://www.itn-emuse.com/vacancies
To apply on: https://www.kuleuven.be/personeel/jobsite/jobs/60065205?hl=en&lang=en
Envisaged Job Starting Date: 01/11/2021
Deadline: Apply up to and including: Friday, October 15, 2021
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