Doctoral researcher at the UGC-DAE Consortium for Scientific Research (Mumbai), working at the intersection of Condensed Matter Physics and Computational Science.
I specialize in the full research stack: from designing cryogenic hardware and automating data acquisition to developing simulation models and AI-driven analysis tools.
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I am a PhD student in Condensed Matter Physics with a focus on Materials Science and Scientific Computing. My goal is to modernize experimental physics by building open-source tools that bridge the gap between lab hardware, data acquisition, and theoretical modeling.
- Looking for: Postdoctoral opportunities in Condensed Matter or Computational Physics.
- Repositories: My primary work is on GitHub. Public repositories are mirrored to GitLab every few days as a backup.
- Contact: Reach me at
prathameshnium[at]duck[.]com.
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PICA is a modular framework I developed to automate high-precision characterization and transport measurements. It standardizes the workflow from Data Acquisition (DAQ) to analysis. Submitted to the Journal of Open Source Software (JOSS). 
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PICA: Instrument Control & Automation
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Hardware reference design for a modular cryogenic platform optimized for high-impedance transport, pyroelectric, and magnetodielectric characterization in PPMS (14T) environments. 
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A privacy-focused AI toolkit leveraging Local LLMs (Ollama). Includes "Orochimaru," a RAG-powered assistant for analyzing academic papers (PDFs) and generating literature reviews. 
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Kusanagi-AI: AI Toolkit for Research
| Repository | Focus Area | Tech Stack |
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| Physics-Simulation-Toolkit | Condensed Matter Simulations: Ising model, Magnetic ordering, Dielectric relaxation. | Python, Jupyter |
| Solid-State-Calculators | Data Analysis: Arrhenius plots, Mott-VRH transport models, Activation energy. | Python, SciPy |
| TupperTransformer | Algorithms: Interactive bitmap math framework for Tupper's self-referential formula. | JavaScript, Math |
| Python-for-OriginPro | Lab Automation: Scripts to automate plotting and data management in OriginLab. | Pandas, OriginC |
- Experimental Physics: Low-temperature transport, Dielectric Spectroscopy, Magnetometry.
- Scientific Computing: Instrument Control (PyVISA), Data Pipelines, Simulation, Local AI (RAG).