Research Fellow (Computational Nanofluidics), ChBE

National University of Singapore; Singapore; On-site; SGD 55,000 - 75,000; Full time

Job summary

The National University of Singapore invites applications for a Postdoctoral Researcher in the Department of Chemical and Biomolecular Engineering. The role will develop computational models for ion-pore electrostatic interactions in electrolyte transport through nanopores, bridging quantum and classical descriptions.

You will combine quantum chemical calculations, MD simulations, and physics-informed machine learning to enable predictive modelling across scales, contributing to next-generation

Qualifications

• PhD in a related field and strong publication record.
• Experience with quantum chemical/electronic structure methods and molecular simulations.

Responsibilities

• Develop and apply computational models for ion-pore electrostatic interactions.
• Bridge quantum and classical descriptions for electrolyte transport in nanopores.
• Collaborate with a team to enable predictive modelling across length/time scales.

Skills

Quantum chemistry

Molecular simulations

Physics-informed ML

Education

PhD in Chemical Engineering

Tools

CP2K

VASP

LAMMPS

Job description

Company description: The National University of Singapore is the national research university of Singapore. Founded in 1905 as the Straits Settlements and the Federated Malay States Government Medical School, NUS is the oldest higher education institution in Singapore

Job Description

Applications are invited for a highly motivated Postdoctoral Researcher role in the group of Prof. Rahul Prasanna Misra in the Department of Chemical and Biomolecular Engineering at the National University of Singapore (NUS).

The successful candidate will develop and apply advanced computational models that capture the complex physics of ion-pore electrostatic interactions for electrolyte transport through nanopores. This would involve developing new theoretical frameworks for molecular simulations involving a combination of quantum chemical calculations, classical molecular dynamics (MD) simulations and physics-informed machine learning. A key objective of this work is to bridge classical and quantum descriptions of confined electrolytes, enabling predictive modelling across length and time scales. More broadly, the project seeks to understand and actively control transport and thermodynamic behaviour in next-generation membrane and nanofluidic systems, with the goal of advancing clean water and energy technologies.

Job Requirements

• PhD in Chemical Engineering, Materials Science, Physics, Chemistry, or a related field
• Experience with quantum chemical and electronic structure methods, and molecular simulation tools such such as CP2K, VASP, LAMMPS, or related packages
• Experience with machine learning or deep learning frameworks such as TensorFlow or PyTorch, particularly for physics-informed or scientific applications, is desirable
• Track record of high quality publications, and the ability to work independently as well as in a team environment