Research Fellow (Materials Generative Design and Testing Framework)
Research Fellow (Materials Generative Design and Testing Framework)
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Job Description
The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational Materials Science Characterisation Materials Science Defence Composite Materials Functional Composite Materials Energy Nanomaterials Low Dimensional Materials Biomaterials Materials Biological Materials Bioinspired Materials and Sustainable Materials.
For more details please view are looking for aResearch Fellow to contribute to the development of novel inorganic materials representations generative design framework and high-throughput simulations including phonon calculations and dynamic stability all the way to calculation of phonon thermal conductivity for active learning. The role will work at the intersection machine learning high-throughput computation and inorganic crystalline materials discovery focusing on accelerating the design and synthesis of novel functional materials.
Key Responsibilities:
Implement data processing pipelines for materials informatics including large-scale data mining curation and processing.
Integrate computational materials science techniques (DFT MD machine learning force fields) with data-driven approaches.
Design and implement high-throughput experimental workflows for thermal conductivity and phonon stability of local phase space for inorganic materials.
Collaborate with interdisciplinary teams including chemists physicists and AI/ML experts to refine generative models with theoretical feedback.
Publish findings in high-impact journals and present research at conferences.
Supervise graduate students and mentor junior researchers in the group.
Job Requirements:
PhD in Materials Science Chemistry Physics Computer Science or a related field.
Hands-on experience with computational materials methods (e.g. DFT molecular dynamics machine learning force field simulations).
Proficiency in Python TensorFlow/PyTorch and scientific computing.
Experience in handling computational datasets.
Demonstrated ability to publish in top-tier journals and work in a collaborative research environment.
Strong problem-solving skills creativity and a passion for materials innovation.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTUEmployment Type
Full-Time
