Research Fellow (Computational Physics)

National University of Singapore; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

The National University of Singapore invites applications for a full-time Postdoctoral Research Fellow position in the Institute for Functional Intelligent Materials (I-FIM). The appointment is a fixed-term contract for two years with potential renewal subject to funding and performance.

Ideal candidates have a PhD in a computational field and proven expertise spanning DFT, transport theory, and machine learning, with hands-on experience in VASP/Quantum ESPRESSO and ML frameworks on GPU

Qualifications

• PhD in Physics, Materials Science, Chemistry, or related field.

Responsibilities

• This role requires hands-on expert command of the full computational toolchain, end to end, the entire pipeline from first-principles defect physics through transport theory to machine-learning-based diagnostics
• Demonstrated ability to personally execute and lead technical work across multiple domains (DFT, transport theory, and machine learning)
• Strong record of peer-reviewed publications spanning computational condensed-matter physics and, ideally, machine-learning applications, commensurate with career stage

Skills

DFT methods

First-principles

Quant. transport

Machine learning

Deep learning

Python

MATLAB

PyTorch

TensorFlow

2D materials

HPC

Publications

Cross-domain leadership

Education

PhD in Physics, Materials Science, Chemistry

Tools

VASP

Quantum ESPRESSO

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

The Institute for Functional Intelligent Materials (I-FIM) is the world's first institute dedicated to the design, synthesis, and application of Functional Intelligent Materials (FIMs). Its global vision is to create a platform to develop I-FIMs with predetermined properties and autonomous, dynamic functionalities which can respond to changing environmental conditions. I-FIM will then investigate the use of such materials for smart applications in various sectors of technology.

At I-FIM, we value the health and wellbeing of our I-FIM community. We aim to facilitate people's journey towards a state of complete physical, mental and social wellbeing, where we realise our own potential and are able to contribute meaningfully to our work and communities. Together, I-FIM helps our people to stay healthy and meaningfully engaged.

I-FIM is inviting applications for a full-time Postdoctoral Research Fellow position. The appointment will be on a fixed-term contract for two years, with the possibility of renewal subject to satisfactory performance and the availability of funding.

Job Qualifications & Requirements

• This role requires hands-on expert command of the full computational toolchain, end to end, the entire pipeline from first-principles defect physics through transport theory to machine-learning-based diagnostics


• PhD in Physics, Materials Science, Chemistry, or a closely related computational field, with a demonstrated track record spanning first-principles methods, quantum transport theory, and machine learning


• Expert, hands-on experience with DFT codes (VASP and/or Quantum ESPRESSO)


• Expert proficiency in Python and/or MATLAB


• Knowledge of T-matrix scattering theory and quantum transport


• Knowledge of effective-medium theory (EMT) and random-resistor-network (RRN) simulation (typically in MATLAB)


• Expert, hands-on experience training deep-learning models with PyTorch and/or TensorFlow on GPU clusters


• Knowledge of 2D materials (transition-metal dichalcogenides such as MoS₂/MoTe₂ preferred) and point-defect physics


• Proficiency with CPU and GPU high-performance/cluster computing environments and scripting for end-to-end computational workflows


• Strong record of peer-reviewed publications spanning computational condensed-matter physics and, ideally, machine-learning applications, commensurate with career stage


• Demonstrated ability to personally execute and lead technical work across multiple domains (DFT, transport theory, and machine learning)


• Prior work spanning the full first principles-to-machine-learning pipeline, ideally demonstrated through publications or projects that combine DFT, transport theory, and ML


• Software engineering practices for reproducible scientific pipelines (version control, structured HDF5 data management, documentation) across a multi-stage computational workflow