The School of Materials Science & Engineering (MSE) is searching for a detailed and responsible Research Fellow (RF) with strong background in materials science and machine learning. This role involves researching the synthesis of high entropy nanostructured composites and utilizing existing machine learning algorithms to build a comprehensive materials database for analysis/reference.
Propose and execute new synthesis approaches to generate and characterize high entropy nanostructured composites for catalysis and carbon capture applications.
Utilize established learning models to analyze materials produced by rapid thermal annealing methods.
Develop and fine-tune machine learning models for predictive and prescriptive analytics.
Possess a PhD degree in Materials Science, computer science, machine learning, or any relevant science and engineering disciplines.
Strong interest in conducting interdisciplinary research, particularly in the intersection of machine learning, materials informatics and materials synthesis.
Strong proven background in machine learning, including experience with established models and data analytics.
Proficiency in standard programming languages such as Python, R, C+ for data analytics.
Excellent and relevant publication records
Excellent communication skills in written and oral English.
Independent scientist and a team player to work effectively in collaborative environments.
We regret that only shortlisted candidates will be notified.
Hiring Institution: NTU
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