Research Fellow (Atomic catalysts and Machine Learning)

Nanyang Technological University; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

NTU Singapore’s School of Physical and Mathematical Sciences seeks a Research Fellow to contribute to research on data-driven discovery of atomic catalysts. The role emphasizes theoretical predictions with DFT and machine learning, plus mechanistic studies using in-situ/operando methods in a multidisciplinary setting.

Applicants should hold a PhD in materials science or applied physics with a focus on renewable energy, and have a strong publication record in electrocatalysis.

Qualifications

• PhD in materials sciences or applied physics (renewable energy focus)
• Experience applying DFT and machine learning to atomic catalysts
• Strong publication record in renewable energy materials, electrocatalysis
• Entry-level candidates are welcome to apply
• Demonstrated motivation for tackling grand challenges in multidisciplinary settings
• Excellent teamwork and ability to support a dynamic, interdisciplinary lab environment

Responsibilities

• Perform theoretical predictions using DFT and machine learning to understand atomic catalysts
• Investigate mechanisms using in-situ and/or operando techniques and complementary theory
• Coordinate collaboration with researchers to maintain progress and support related experiments
• Support project management and lab infrastructure, including procurement and equipment setup
• Provide regular progress reports to the PI
• Prepare a proposal based on project results for external grants

Skills

DFT

Machine learning

Publications in renewable energy

Education

PhD in materials science or applied physics

Job description

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