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Smart bioactive materials for sustainable nutrition and crop enhancement: Integrating AI-driven design with functional biopolymers

This interdisciplinary PhD project explores the development of smart bioactive materials that can simultaneously enhance nutritional value in food systems and/or promote plant health in agriculture. The research integrates materials science, machine learning, and nutrition, offering a unique opportunity for candidates from diverse backgrounds to contribute to sustainable innovation. The project builds on recent advances in biopolymer-based materials. The PhD student will design and synthesize composite materials using bioactives, optimizing their structure and functionality for dual applications. A key innovation in this project is the use of machine learning-driven materials informatics. Machine learning models will be employed to predict the relationships between material composition, structure, and performance. This data-driven approach will accelerate the discovery and refinement of formulations, enabling targeted improvements in bioactivity, stability, and scalability. The student will conduct experimental testing to evaluate the materials' effectiveness in both food and agricultural contexts. This includes assessing nutrient delivery in food matrices and measuring crop growth in controlled environments. The project also includes a sustainability assessment, examining the environmental impact and life-cycle of the developed materials. This PhD offers broad accessibility to candidates with backgrounds in materials science, chemistry, food science, biotechnology, plant sciences, AI/data science, or chemical/environmental engineering. The outcomes of this research have the potential to contribute to global challenges in food security, sustainable agriculture, and green materials development.

Identifying high value compounds during a yeast fermentation of food waste

This PhD research project focuses on yeast strain development for use as a fortified strain in the fermentation industry with potential as a standalone product. The research involves identifying high value metabolites produced by yeast during fermentations and testing the feasibility of upcycling vegetable loss to formulate growth media for microorganisms, lowering production costs and promoting circular economy. The project integrates microbial fermentation optimization and builds into an advanced artificial intelligence database. Research activities include designing and conducting fermentation optimization of vegetable and fruit sidestreams, determining changes in yeast during fermentation using analytical tools such as ICP-OES, XRF, and NMR, seeking opportunities to develop yeast products in collaboration with commercial stakeholders, and generating independent research ideas. The position involves preparing papers for publication in leading international journals and disseminating research results through recognized conferences.