Research Fellow (High-Throughput Synthesis)
NTU Main Campus, Singapore
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Nanyang Technological University
Nanyang Technological University is one of the top universities in Singapore offering undergraduate and postgraduate education in engineering, business, science, humanities, arts, social sciences, education and medicine.The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological Materials, Bioinspired Materials and Sustainable Materials.
We are looking for a Research Fellow to support our ongoing efforts in accelerating the discovery of novel solid-state materials through high-throughput synthesis, automation, and data-driven approaches. The candidate will bring expertise in the synthesis and magnetic characterization of complex intermetallic systems, which will be leveraged to develop and optimize scalable experimental protocols across diverse material families.
This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous experimentation platforms to push the boundaries of functional materials discovery.
Key Responsibilities:
Design and execute high-throughput synthesis workflows for solid-state materials, including intermetallics, chalcogenides, and oxides.
Operate and maintain automated synthesis platforms, including programmable furnaces, robotic sample handling systems, and reaction chambers.
Perform structural and compositional characterization using techniques such as XRD, SEM-EDS, and related in-house or collaborative methods.
Analyze structure–property relationships and contribute to feedback loops that guide AI-based predictive models.
Document and manage experimental data in a structured and reproducible manner to enable integration with ML pipelines.
Collaborate closely with computational scientists, postdoctoral researchers, and lab engineers to scale experimental throughput and accelerate material screening.
Contribute to scientific publications and grant reporting in collaboration with the principal investigator.
Job Requirements:
PhD (with strong research experience) in Chemistry, Materials Science, or related disciplines.
Extensive hands-on experience in solid-state synthesis, including arc melting, annealing, and powder metallurgy techniques.
Strong background in intermetallic systems, phase diagram analysis, and crystallography.
Familiarity with high-throughput or combinatorial synthesis approaches is a strong plus.
Experience in handling and analysing data from XRD, SQUID/VSM magnetometry, and other characterization tools.
Excellent documentation, communication, and collaboration skills.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTU* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Chemistry Engineering Machine Learning PhD Pipelines Research
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