This Computing and Data Science Training for Materials Innovation, Discovery, and AnalyticS (NRT-MIDAS) is a new traineeship for doctoral students in Computer Information Sciences, Electrical and Computing Engineering, Chemical Engineering, Materials Science and Engineering, Biomedical Engineering, and Chemistry. In this traineeship students will learn to use high performance computing and data science to discover, innovate, and analyze new synthetic and biologically relevant polymeric materials with tailored properties and function. Through the research and educational activities in this NRT, trainees and advisors will also improve equitable access to the soft materials and polymers data, science, and technology knowledge that could reduce costs by eliminating trial-and-error based materials exploration and promote sustainability through lean manufacturing.
Motivation for this NRT traineeship: Polymers are ubiquitous in everyday products (e.g., food packaging, paints, pipes, etc.) and in advanced technologies (e.g., microelectronics, organic solar cells, drug/gene delivery, etc.). As the functional wish-list for these synthetic and biologically-relevant polymeric materials grows in demand and complexity, there is a need for techniques that can accelerate design of polymers with specific functional properties, and improve speed and efficacy of polymer synthesis and characterization. Addressing this need requires a combination of tools and perspectives from materials science, polymer chemistry, physics, and engineering, process systems engineering, computer science, data science, and artificial intelligence. Even though there is a great opportunity in the amalgamation of disciplines for data-driven materials innovation, there are a variety of barriers to data‐driven polymer and soft materials science. These challenges include (but not limited to) integration of experimental and computational data, data life, standardization in data sharing, and the gap between R&D in industry and academic laboratory efforts. To address these challenges and to better support shifts in research and development (R&D) from traditional trial-and-error materials experimentation to data-driven polymer design and manufacturing requires a new type of workforce that is well trained in both computational methods and tools (i.e., high-performance computing [HPC] and data science [DS] as well as the materials focused domain science (i.e., polymer modeling and simulations, synthesis, characterization, and processing) [MAT].
This NRT graduate traineeship will advance the research theme of HPC and DS driven polymer innovation, discovery, and analytics (i.e., polymer informatics) by training a cohort of researchers with necessary cross-disciplinary technical skills, professional skills, and networks (Figure below) to become the next generation of leaders well versed in HPC, DS and MAT.
Figure: Our NRT-MIDAS traineeship blends technical training and professional development for doctoral students doing convergent high-performance computing (HPC), data science (DS), and polymers and soft materials (MAT) research.
The NRT core faculty team aims to provide graduate training in polymer informatics through convergent research and technical training elements in polymer and soft materials modeling, simulations, experiments, computing, and data analytics, supplemented with close and regular interactions with mentors from relevant industries, national labs, and members in academia.
Our integrated approach to the technical and professional skills development of doctoral trainees will prepare them to be career-ready for multiple career paths upon graduation.
Interested PhD students who wish to apply for this traineeship should visit the ‘How to apply’ link in the dropdown menu.