Research Fellow (Atomic catalysts and Machine Learning)
At a glance
Mid-level Machine Learning Engineering role at Ntu. NTU Main Campus · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Use DFT and machine learning to predict atomic catalysts, then explain how they work
- Investigate catalyst mechanisms through in-situ or operando methods and theoretical calculations
- Coordinate with project researchers and assist the group with related experiments
- Support project operations by sourcing equipment, establishing battery fabrication and characterization setups, and preparing reports
- Share progress with the PI and write an external grant proposal based on project findings
What you bring
- A PhD in materials science or applied physics, focused on renewable energy
- Demonstrated DFT and machine learning experience, particularly for atomic catalysts
- A strong publication record in renewable energy materials, especially electrocatalysis
Who this fits
This role suits an early-career researcher ready to tackle multidisciplinary research on atomic catalysts. You’ll work with other researchers and support experiments, project operations, and lab infrastructure at NTU’s main campus in Singapore.
From the employer
Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and photonics. Over the years, SPMS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers.
Join the laboratory of Fan Hongjin at SPMS. We are seeking a Research Fellow to contribute to a project focused on focused on data-driven discovery of atomic catalysts.
Key Responsibilities:
Theoretical predictions using DFT and machine learning, and mechanistic understanding of atomic catalysts
Investigation of mechanism by in-situ and/or operando techniques and theoretical calculations
Coordinate the collaboration with other researchers in this project to maintain steady progress, and provide support to the research group on related experiments
Provide support to the project management and lab infrastructure, including procurement, setting up battery fabrication line, set up equipment for in-situ and/or operando characterizations, annual and final project reports
Make regular progress reports to PI
Write one proposal based on results obtained from this project for external grant
Job Requirements:
A PhD degree in material sciences or applied physics, with focus on renewable energy
Evidenced experience in DFT and machine learning especially its application in atomic catalysts
Strong publication record on the topic of renewable energy materials especially electrocatalysis
Entry level candidates are welcome to apply
Strong motivation to undertake grand challenges in multidisciplinary subjects
Excellent team player who is capable and willing to provide supports in a dynamic and interdisciplinary environment
The College of Science seeks a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTU