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)
- Preferred to have prior work spanning the full first principles-to-machine-learning pipeline, ideally demonstrated through publications or projects that combine DFT, transport theory, and machine learning
- Preferred to have software engineering practices for reproducible scientific pipelines (version control, structured HDF5 data management, documentation) across a multi-stage computational workflow