Research Fellow (Materials Data Scientist)

The School of Materials Science & Engineering (MSE) is seeking a methodical and reliable Research Fellow (RF) with a robust background in materials science and machine learning. The role involves researching the production of high entropy nanostructured composites and using current machine learning algorithms to create a comprehensive materials database for analysis/reference. Key Responsibilities: 1. Suggest and implement new synthesis methods to create and examine high entropy nanostructured composites for catalysis and carbon capture applications. 2. Employ established learning models to analyze materials created by rapid thermal annealing methods. 3. Design and refine machine learning models for predictive and prescriptive analytics. Job Requirements: 1. Hold a PhD degree in Materials Science, computer science, machine learning, or any related science and engineering disciplines. 2. Strong interest in carrying out interdisciplinary research, particularly at the intersection of machine learning, materials informatics, and materials synthesis. 3. Solid proven background in machine learning, including experience with established models and data analytics. 4. Proficiency in standard programming languages such as Python, R, C+ for data analytics. 5. Excellent and relevant publication records. 6. Excellent communication skills in both written and oral English. 7. Ability to work as an independent scientist and collaboratively in a team environment. Please note that only shortlisted candidates will be contacted.