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Souryadeep Bhattacharyya

Majored in Chemical Engineering Ph D
Georgia Institute of Technology, Class of 2018
From Greater Atlanta Area, GA
I am a result-oriented Chemical Engineering (ChE) Postdoctoral Researcher specializing in porous materials, stability testing, adsorption, process separations, with experience in experimental design, data analysis, equipment construction, troubleshooting, safety & hazard analysis, project supervision and mentorship. I am a proactive multitasker with high research productivity (17 publications, 2 patents), both working independently and through collaborations. My previous research experience includes electrospinning, polymer nanoparticle synthesis, and synthetic biology. My communication, leadership, and time-management skills were recognized in my selection as Leadership Fellow and being awarded the International Student Leadership Award by Georgia Institute of Technology (GT) for contribution to campus life. In addition, I am an accomplished Indian Classical Instrumental musician and love creating innovative musical compositions and spreading happiness in listeners (www.souryasarod.com). I am actively looking for research scientist/process engineering roles in the chemical engineering industry and can be contacted at : souryadeepb@gatech.edu or +1-404-834-4767 Google Scholar : https://scholar.google.com/citations?user=O01w67gAAAAJ&hl=en&oi=ao
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Souryadeep Bhattacharyya Receives Degree from Georgia Tech

Souryadeep Bhattacharyya of , India, has earned a Doctor of Philosophy in Chemical Engineering from the Georgia Institute of Technology in Atlanta. Bhattacharyya was among approximately 3,600 unde...

February, 13 2019 - Verified by Georgia Institute of Technology
Postdoctoral Research Fellow at Georgia Institute of Technology
Project: Predicting durability of Zeolitic Imidazolate Framework (ZIF) materials towards acid gases under process conditions Supervisor: Prof. Sankar Nair Determined dependence of humid acid gas induced degradation reaction rate of ZIFs on relative humidity through statistical analysis. Predicted material stability outside training dataset range with experimental validation of model. Expanding predictive approach to more MOFs and process variables to improve efficiency of stability testing.
October 2018 - Present

Graduation

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