Research Fellow (Computational Nanofluidics), ChBE

National University of Singapore; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

The National University of Singapore invites applications for a Research Fellow (Computational Nanofluidics) in the Chemical and Biomolecular Engineering department. The role focuses on developing computational models for ion-pore electrostatics and electrolyte transport through nanopores, integrating quantum chemistry, MD simulations and physics-informed machine learning.

The successful candidate will bridge classical and quantum descriptions across length/time scales, contributing to

Qualifications

• PhD in Chemical Engineering, Materials Science, Physics, Chemistry, or a related field.
• Experience with quantum chemical methods and electronic structure calculations.
• Experience with molecular simulation tools (CP2K, VASP, LAMMPS) and ML frameworks (TensorFlow, PyTorch) desirable.
• Track record of high-quality publications and ability to work independently and in teams.

Responsibilities

• Develop and apply computational models for ion-pore electrostatic interactions in electrolyte transport through nanopores.
• Bridge classical and quantum descriptions to enable predictive modelling across length and time scales.
• Contribute to understanding and control of transport and thermodynamic behaviour in membrane and nanofluidic systems.
• Advance clean water and energy technologies through computational studies in the Misra group at NUS.

Job description

Job Title: Research Fellow (Computational Nanofluidics), ChBE

University-Level Unit: College of Design and Engineering

Faculty/Department-Level Unit: Chemical and Biomolecular Engineering

Employee Category: Research Staff

Location_ONB: Kent Ridge Campus

Posting Start Date: 24/08/2026

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 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