Publisher
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Progna Banerjee

8 months ago

Fully Funded PhD Positions in Nanomaterials, Solid Electrolytes, and Machine Learning for Battery Innovation Loyola University Chicago in United States

Degree Level

PhD

Field of study

Computer Science

Funding

The positions are fully funded PhD openings. Funding covers tuition and provides a stipend, as is standard for fully funded PhD positions in the United States. No application fees are required. Specific stipend amounts are not mentioned.

Deadline

Expired

Country flag

Country

United States

University

Loyola University Chicago

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Keywords

Computer Science
Machine Learning
Chemistry
Materials Science
Nanotechnology
Artificial Intelligence
Transmission Electron Microscopy
Energy Materials
Physics

About this position

Fully funded PhD positions are available in the P. Banerjee Energy Lab at Loyola University Chicago, starting Fall 2026. The lab, led by Professor Progna Banerjee, focuses on the theory-guided, data-driven design of colloidal superionic electrolytes and interphases for safe, scalable battery innovation. Research areas include synthetic inorganic chemistry, advanced electron microscopy, machine learning, and electrochemistry, all aimed at accelerating the discovery of next-generation solid electrolytes and nanoscale superionic conductors.

PhD Position 1: This position centers on the synthesis of nanocrystals with unusual symmetries and advanced transmission electron microscopy (TEM) studies. The successful candidate will lead air-free colloidal synthesis and advanced TEM-enabled structure–transport mechanism studies in superionic/solid electrolyte nanomaterials. Access to multiple electron microscopes is provided for advanced techniques. Essential qualifications include strong fundamentals in solid-state chemistry, physics, or materials science, with a deep understanding of defects, phase behavior, and diffusion/transport. Preferred skills include glovebox/Schlenk techniques, XRD, EIS, and TEM readiness or interest. Technical experience with tangential nanomaterial synthesis techniques alone is not sufficient without in-depth knowledge of colloidal nanocrystal synthesis fundamentals.

PhD Position 2: This position focuses on building a machine learning (ML)/artificial intelligence (AI)-assisted discovery pipeline for nanoionics. Essential qualifications include strong programming skills in Python and experience with ML/data workflows, version control, and reproducible scientific computing. Preferred experience includes materials informatics, signal processing, uncertainty quantification, and scientific computing.

The P. Banerjee Energy Lab integrates theory, automation, and AI to discover new families of nanoscale ionic materials, bridging atomic-level defect engineering with scalable solution processing. The lab's vision is to establish design rules for nanoscale superionic conductors, advancing both fundamental solid-state ionics and applied energy technologies, with the ultimate goal of enabling safe, high-energy-density batteries and clean hydrogen systems.

Funding for these PhD positions is fully provided, covering tuition and stipend. There are no application fees. The application deadline is March 31, 2026. Applicants should complete the online application at Loyola University Chicago and then email Professor Banerjee with their application number and research interests. More information about the lab's research can be found at pbanerjeelab.com.

Keywords: nanomaterials, solid electrolytes, colloidal synthesis, machine learning, artificial intelligence, electron microscopy, materials informatics, battery innovation, energy materials, superionic conductors.

Funding details

The positions are fully funded PhD openings. Funding covers tuition and provides a stipend, as is standard for fully funded PhD positions in the United States. No application fees are required. Specific stipend amounts are not mentioned.

What's required

Applicants must have strong fundamentals in solid-state chemistry, physics, or materials science, particularly in defects, phase behavior, and diffusion/transport. For the nanocrystal synthesis position, in-depth understanding of colloidal nanocrystal syntheses is essential; mere technical experience with tangential techniques is insufficient. Preferred skills include glovebox/Schlenk techniques, XRD, EIS, and TEM. For the ML/AI position, strong programming skills in Python and experience with machine learning, data workflows, and version control are essential. Preferred experience includes materials informatics, signal processing, uncertainty quantification, and scientific computing. No specific GPA or language test requirements are mentioned.

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Professors

Professor

University Name
Country flag

United States

Research Interests

Materials Chemistry

Condensed Matter Physics

Materials Science

Metal Chemistry

Phase Diagram