Perform electrical and magnetic transport measurements; support sample mounting, contacting, and probe-station preparation; and provide timely measurement feedback for process optimization, device testing, yield/reliability assessment, and project reporting.
Analyze experimental data, generate graphs and tables, and collaboratively support the evaluation of magnetic, transport, and thermal properties of thin-film/device samples. Prepare technical summaries, progress reports, or research outputs when required.
Support laboratory operations and project coordination, including equipment maintenance, safety/documentation compliance, procurement support, experiment scheduling, progress tracking, and user coordination/training.
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Fabricate and characterize 2D amorphous carbon-based devices and test structures.
Evaluate dielectric, structural, mechanical, electrical, and diffusion-barrier properties of MAC films.
Develop application use cases for 2D amorphous carbon films in ultralow-k dielectrics, interconnect integration, diffusion barriers, magnetic media, and protective coatings.
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Establish structure–property relationships using materials characterisation to guide formulation design.
Build data-driven design tools that translate empirical formulation knowledge into material selection and optimization for various industrial applications.
Contribute to lab automation, data infrastructure (SQL-based), and integrated experimental–ML workflows for accelerated discovery.
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Conduct regular cost reviews with Executive Management and thoroughly analyze every aspect of your product costs.
Plan and manage tooling budget and spend.
Architect and establish a solid Supplier Plan of Record (SPOR) for the product(s) along with Engineering, Software, Supply Chain, and other Ops functional teams to ensure time-to-market, supply continuity, manufacturability, scalability, and sustainability.
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Oversee scaling-up process engineering, including process optimization, troubleshooting, and collaboration with engineering/manufacturing partners where necessary.
Supervise and guide team members in testing and characterization, ensuring data quality, reproducibility and adherence to safety and laboratory standards.
Organize and manage experimental data, reports, and technical documentation for internal reviews, external collaborators, and reporting purposes.
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Prof Shyue Ping Ong’s Materialyze.AI lab at the Department of Materials Science and Engineering aims to pioneer the integration of theory, experiments, and AI to accelerate the discovery and deployment of breakthrough materials. We are recruiting highly motivated Research Fellows who are passionate about accelerating materials innovation through scientific rigor, creative thinking, and interdisciplinary collaboration. We welcome applicants with expertise in materials theory, experiments, AI for materials, or—ideally—a combination spanning these domains.• Theory & AI in Materials Design - Develop and apply machine learning and AI models (e.g., ML interatomic potentials, generative design, reinforcement learning) to predict and design materials. - Perform first-principles and molecular dynamics simulations to model structural, thermodynamic, and electronic properties. - Contribute to open-source software, benchmarks, and datasets that advance the global materials community.• Experiments & AI Integration - Synthesize and process functional materials relevant to batteries, aerospace alloys, and semiconductors using solid-state, solution, or thin-film methods. - Apply advanced characterization techniques (XRD, TEM, SEM, spectroscopy, electrochemistry, etc.) to probe structure–property relationships. - Collaborate with theory and AI researchers to validate predictions, generate datasets, and develop high-throughput/automated experimental workflows. - Experience in developing autonomous laboratory systems is a strong plus.
• PhD in Materials Science, Physics, Chemistry, Chemical Engineering, Mechanical/Aerospace Engineering, or a related field.• Strong publication record demonstrating creativity, rigor, and domain expertise.• Proven ability to work in interdisciplinary teams.• For experimental applicants: hands-on experience with synthesis and characterization equipment.• For theory/AI applicants: experience with DFT, MD, MLIPs, or AI/ML frameworks.