S. Swayamjyoti

About

As a doctoral graduate of ETH Zurich, which is one of the leading research institutions in the world, I intend to leverage my expertise in engineering, computational materials science, computational mechanics, numerical mathematics, physics, computational sciences, and in simulations and modelling at a significantly advanced level. My past research and studies have been highly mathematical in nature. I would like to bring my expertise in computer simulations using numerical schemes for suitable applications in the scientific world in collaboration with experimentalists.

Employment

• Indian Institute of Technology (IIT) Bhubaneswar Scientific Researcher
  2019 - 2022
• Brown University Post-Doctoral Research Associate
  2017 - 2017
• ETH Zurich Doctoral Researcher
  2012 - 2016
• University of Stuttgart (Germany) and CIMNE Barcelona (Spain) Master of Science Student (fully funded by Erasmus Mundus scholarship)
  2009 - 2012

Education

• ETH Zurich: the Swiss Federal Institute of Technology PhD degree: Dr. sc. ETH Zurich (fully funded by Swiss National Science Foundation)
  2012 - 2016
• University of Stuttgart and CIMNE Barcelona Master of Science (fully funded by Erasmus Mundus scholarship)
  2009 - 2012
• National Institute of Technology Karnataka, Surathkal Bachelor of Technology
  2004 - 2008

Projects & Funding

Projects & funding information is unavailable.

Publications (9)

• Quasivoids in Polydisperse Glassy Systems With Atomistic PEL Exploration and Iso‐Configuration Method
  Proceedings in Applied Mathematics and Mechanics 2026 DOI: 10.1002/pamm.70144
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• Molecular dynamics simulation of salt diffusion in constituting phosphazene-based polymer electrolyte
  Journal of Physics: Condensed Matter 2024 DOI: 10.1088/1361-648x/ad6727
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• Deep Learning-Based Optimization of Piezoelectric Vibration Energy Harvesters
  AIAA SCITECH 2022 Forum 2022 DOI: 10.2514/6.2022-2142
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• Machine learning in materials modeling - Fundamentals and the opportunities in 2D materials
  arXiv 2020
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• Multi-class classification of vulnerabilities in smart contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing
  arXiv 2020
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• Performance traits of a newly proposed modularity function for spatial networks: Better assessment of clustering for unsupervised learning
  IOP Conference Series: Materials Science and Engineering 2020 DOI: 10.1088/1757-899X/872/1/012017
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• Visual machine learning: Insight through eigenvectors, chladni patterns and community detection in 2D particulate structures
  arXiv 2020
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• Local structural excitations in model glass systems under applied load
  Physical Review B 2016 DOI: 10.1103/PhysRevB.93.144202
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• Local structural excitations in model glasses
  Physical Review B - Condensed Matter and Materials Physics 2014 DOI: 10.1103/PhysRevB.89.224201
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