The IGF project ProteinPredict4Food is being carried out jointly with the Fraunhofer IVV and aims to predict the suitability of protein ingredients for use in plant-based dairy and sausage alternatives based on their chemical, physicochemical and techno-functional properties.
The TUM part of the project (FS II) focuses on milk and yogurt alternatives and includes the development of model formulations, their analytical characterization, and the prediction of application capability using supervised machine learning.
Your tasks
- Development and production of model formulations for plant-based drinks and yogurt alternatives (acid-induced gelation, emulsion and homogenization processes)
- Analytical characterization of product properties (including rheology, texture, syneresis, color, viscosity, particle size distribution, dispersion stability)
- Data analysis and modeling using supervised machine learning to predict application capability
- Development of a requirements catalog for protein ingredients in dairy alternatives
- Close collaboration with the project partner Fraunhofer IVV and with industry representatives from the project advisory committee
- Presentation and publication of research results at scientific conferences and in specialist journals
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TUM PhD Scholarship
Aim and Benefits of TUM PhD Scholarship
- A highly topical research subject at the interface of food technology, plant nutrition and computer-aided modeling.
- Participation in an interdisciplinary research network with direct industry links
- Modern laboratory infrastructure at the Freising-Weihenstephan science campus
- Opportunity to pursue a doctorate at the TUM School of Life Sciences
- Payment according to TV-L
Requirements for TUM PhD Scholarship Qualification
- Above-average university degree (M.Sc.) in food technology, food chemistry, nutritional science, bioprocess engineering, bioinformatics or a related field
- Solid knowledge of food processing, ideally in the area of ??plant proteins or fermented products.
- Experience with instrumental analysis (e.g., rheology, particle size analysis, texture measurement)
- Strong interest in data analysis, statistical modeling and machine learning (e.g. in Python)
- Structured and independent work style as well as enjoyment of interdisciplinary teamwork • Very good German and English skills, both written and spoken
Application Deadline
Not SpecifiedHow to Apply
- Please send your complete application documents (cover letter, CV, certificates) in one PDF file via email to [email protected].
For more information, kindly visit TUM scholarship webpage.