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.
The job is for automated high-throughput experimentation and robotic development, running AI-driven automated worklows for thin film development, electrical and opto-electronic characterization as well as device applications. The RF will be responsible for building a fully automated robotic platform to execute the scientific goals laid out in the project, with multi-layer perovskite film deposition and crystallization, followed by rapid characterization to create a database (primarily building upon the SpinBot system we have purchased from SciPrios: https://www.sciprios.de/lab-automation/spincoating-robots/ ). The RF is also expected to execute optimization algorithms developed by the team to identify relevant regions of interest in the input parameter space. Python programming, database management and robotic interfacing (both with robots and electrical and mechanical equipment) is necessary for this job.
The work is critical in the goal of materials-by-design, especially considering sparse, yet well-labelled materials science datasets. The work performed here will continue to maintain NTU’s leadership in the space of AI for materials science, especially with digital discovery and data-driven learning being pioneering areas of research.
Key Responsibilities:
The Research Fellow is expected to work in the space of High-Throughput experimentation and automated robotics for synthesis, thin film deposition and electrical and opto-electronic characterization. Specifically, the RF is expected to work on the following areas of research:
- Building a self-driving lab with robots, spin-coater and/or doctor blade coating systems, integrated with sample transfer and electrical and opto-electronic characterization.
- Operating and maintaining the robotic systems and working with protocols, interfacing with characterization equipment, environmental chambers, and a data management system.
- Execute optimization algorithms that allow for fully automated experimentation, working closely with other members of the team.
- To assist in the creation and maintenance as well as performance of experiments in a self-driving thin film lab along with driving the scientific goals of the project, hypothesis generation and working closely with a 4-member PhD team.
Job Requirements:
- PhD degree in materials science, physics, chemistry, electrical engineering and/or related field
- Strong previous experience in building and assembling robotic systems, especially in materials research is critical.
- Experience in solution-based materials deposition is necessary, especially in halide-perovskite based systems.
- Skills and experience in quantitative research, robotics and electrical characterization and interfacing.
- Expertise in materials characterization techniques is necessary.
- Good analytical and academic writing skills
- Must also be informed in data management
- A good team player with effective communication skills and the ability to work closely with faculty, staff and external collaborators
- Good project management skills
- Entry level candidates are welcome to apply
We regret to inform that only shortlisted candidates will be notified.