PhD Recruitments in Machine Learning Boosted Computational Heterogeneous Catalysis

Chemical Engineering at UMass Lowell 

Dr. Fanglin Che research group is currently recruiting multiple Ph.D. candidates, who will be working on the thesis related to machine learning boosted computational heterogeneous catalysis, such as electrocatalysis, plasma catalysis and field-enhanced catalysis.

Dr. Fanglin Che research group is in the department of chemical engineering at University of Massachusetts Lowell.

The candidate(s) will apply deep learning assisted density functional theory, and/or microkinetic modeling, and computational fluid dynamic simulations to facilitate the design of novel catalytic/electrocatalytic materials with tailored properties. The successful candidate(s) will develop data-driven multi-scale simulations (from atomic to continuum scale) for material properties predicting, such as plasma catalysis, electrocatalysis and field-enhanced catalysis. The group webpage is:

Ph.D. Recruitment will be with rolling base。If you are interested, please send your CV, transcripts, TOEFL (or IEITS) to:

The successful candidate will be:

(1) Chemical Engineering or related Major in B.S. and/or M.S.

(2) GPA is over 3.2

(3) Have strong interests in Machine Learning and Computational Catalysis

(4) If the candidate has experience on machine learning, or density functional theory, or microkinetic model, or computational fluid dynamics will be a plus.

Dr. Fanglin Che joined in Chemical Engineering department at UMass Lowell as an Assistant Professor in September, 2019. Dr. Che earned her Ph.D. in Chemical Engineering at Washington State University in December, 2016, under the advisement of Prof. Jean-Sabin McEwen. From 2017 to 2018, she worked on AI-guided electrocatalysis with Prof. Edward Sargent at University of Toronto as a Postdoctoral Researcher. From 2018 to 2019, she worked on computational fluid dynamic simulation for microwave heating as a Postdoctoral Researcher in the Department of Chemical and Biomolecular Engineering at University of Delaware in Prof. Dionisios G. Vlachos’s laboratory.



PI Che research group is focusing on establishing their leadership on Artificial Intelligence guided Multi-Physics and Multi-Scale simulations for renewable energy powered process intensification and electrified systems. PI Che focuses on the systems, including electric field-enhanced catalysis, plasma, microwave catalysis, and electrocatalysis. The overall goal of PI Che research group is to reduce greenhouse gas emissions, net-zero carbon process, carbon capture, removal, storage, and utilizations, and zero-carbon fuel and chemical generations using interpretable, physics-informed machine learning promoted material design and operation design.

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Funding Acknowledgements