Research Fellow (Self-Driving Labs for Proteins)

About TUMCREATE TUMCREATE is a multidisciplinary research platform of the Technical University Munich (TUM) at the Singapore Campus for Research Excellence and Technological Enterprise (CREATE). We are joining forces with universities, public agencies, and industry for the advancement of future technologies. Our large-scale Food Science program, ‘Proteins4Singapore,’ funded by Singapore’s National Research Foundation (NRF), is a collaborative effort between the Technical University Munich (TUM), Nanyang Technological University (NTU), the Singapore Institute of Technology (SIT), and A*STAR’s Singapore Institute of Food and Biotechnology Innovation (SIFBI). As part of the Proteins4Singapore team, you will work alongside renowned scientists in plant and food technology, food chemistry, materials science, and business sustainability. Through interdisciplinary collaboration, the program aims to develop state-of-the-art methods and techniques to ensure a sustainable protein supply for highly urbanized environments. Please visit www.tum-create.edu.sg for more information about TUMCREATE. Job Summary We are seeking a highly motivated and skilled Research Fellow supervised by Assistant Professor Leonard Ng Wei Tat from Nanyang Technological University. As part of the Proteins4Singapore project, the Research Fellow will contribute to the research topic of “High-Throughput Optimisation of Sustainable Protein Extraction”, which aims to employ high-throughput experimentation and self-driving laboratory (SDL) methodologies to systematically discover and optimise protein extraction conditions from soybeans and microalgae. This position plays a critical role in advancing sustainable protein solutions for urban environments through interdisciplinary collaboration, drawing expertise from materials science, chemical engineering, food science, and artificial intelligence. This is a fixed term contract until March 2027. Key Responsibilities Design and execute high-throughput experimental campaigns to optimise protein extraction from soybeans and microalgae, systematically varying process parameters including solvent composition, pH, temperature, and mechanical treatment conditions. Develop and integrate automated or semi-automated workflows for sample preparation, extraction, and analytical characterisation, leveraging laboratory automation platforms such as liquid-handling robots and automated plate-reader systems. Apply self-driving laboratory (SDL) methodologies — including Bayesian optimisation, active learning, and closed-loop experimental design — to accelerate the identification of optimal extraction protocols. Characterise extracted protein fractions with respect to yield, purity, and functional properties (e.g., solubility, emulsification, foaming behaviour). Build and maintain experimental databases and data pipelines to support machine learning model training and data-driven decision-making within the SDL framework. Collaborate with cross-disciplinary tea