We are the "Computational Nanotechnology for Energy and Water" research group in the Department of Chemical Engineering at the Indian Institute of Science
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
NPTEL-ML
August 19, 2025 – October 13, 2025
Material for NPTEL course on ML for Core Engineering Disciplines
View ProjectSTRONG
February 20, 2024 – October 5, 2024
String Representations of Nanopore Geometry in 2D Materials
View ProjectCOF_growth_model
September 14, 2023 – March 8, 2024
This repository outlines a kinetic model and a kinetic Monte Carlo simulation to describe the growth of a 2D polymer, also known as a covalent organic framework
View ProjectMOCVD-Model-MoS2
August 14, 2023 – August 18, 2023
Fully Ab Initio Mechanism of Shape and Size Evolution During the Chemical Vapor Deposition of MoS2 Monolayers
View ProjectPB-LJ
December 21, 2022 – July 23, 2023
Poisson-Boltzmann theory of electrical double layers with van der Waals and soft repulsive interactions
View ProjectStableNanopores
October 8, 2022 – April 29, 2023
A library of all possible stable nanopores in graphene, generated using Redelmeier's algorithm, that allows searching based on their geometrical properties
View ProjectMLforNanopores
March 29, 2022 – June 7, 2025
Machine learning applied to the formation of nanopores via silicon-catalyzed electron-beam etching in graphene
View Projectnanopore_isomers
February 23, 2021 – February 23, 2021
nanopore_isomers — GitHub repository
View ProjectoptimizeOERmechanism
October 25, 2020 – October 25, 2020
Automated electrocatalytic mechanism enumeration for the oxygen evolution reaction
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on academic and research-oriented applications in materials science and chemistry. While this demonstrates strong analytical capabilities, the direct applicability to a typical industry Data Scientist role might require a transition. The breadth of technologies used (Matlab, Python, Jupyter Notebook) is good, but there's no explicit mention of common data science libraries (e.g., scikit-learn, pandas, TensorFlow/PyTorch) or big data technologies, which could indicate a gap in industry-standard tooling. The candidate's experience level is listed as 0, which contradicts the depth of projects, suggesting a potential misclassification or a focus on research without formal industry experience.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.