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 teams to integrate experimental findings with computational models, contributing to the project's long-term goals of advancing sustainable food technologies.
- Engage in collaborative research, data analysis, and interpretation, while contributing to the writing of research papers, reports, and presentations for dissemination at conferences and in peer-reviewed journals.
- Participate in lab meetings, seminars, and collaborative research discussions to align with project goals and objectives.
- Supervise junior group members including undergraduate and doctoral students.
Key Competencies
- Demonstrated experience in high-throughput experimentation, including design of experiments (DoE), combinatorial screening, or parallel synthesis and processing workflows.
- Experience with self-driving laboratory platforms, autonomous experimentation, or closed-loop optimisation systems is highly desirable.
- Solid background in protein science, food science, biochemistry, or chemical engineering, with hands-on experience in protein extraction and characterisation methodologies.
- Proficiency in data analysis and scientific programming (Python preferred), with familiarity with machine learning tools and frameworks relevant to experimental optimisation.
- Familiarity with laboratory automation hardware (e.g., liquid-handling robots, automated plate readers, robotic workstations) is a strong advantage.
- Strong analytical skills, with experience in using data science and AI tools for experimental data analysis and interpretation.
- Excellent communication skills, both written and verbal, in particular communication of complex scientific concepts to both specialists and non-specialists in a multidisciplinary research environment.
- Proven ability to conduct independent research and collaborate in multidisciplinary teams including working effectively within deadlines.
- Self-motivated and adaptable, with strong problem-solving skills and the ability to thrive in a dynamic, fast-paced research environment.
Qualification Requirements
- Ph.D. in Chemical Engineering, Materials Science, Food Science, Biochemistry, Chemistry, Bioengineering, or cognate discipline from a reputable university.
- At least 1 year of experience in high-throughput experimentation, self-driving laboratories, or AI for Science.
- Excellent written and verbal communication skills in English.
What we offer
- A collaborative and inclusive world class research environment.
- Access to state-of-the-art laboratory facilities and equipment.
- Attractive and competitive remuneration commensurate with qualifications and experience.
- Opportunities for professional development and career growth.
- Engagement with a leading research team committed to advancing an emerging topic of global relevance.
Applications
Please send your complete application, including cover letter, CV, university transcripts, and degree certificates, to [Confidential Information].
Only shortlisted candidates will be contacted.
We are looking forward to your application!
