MSE Tenure-Track Positions 2025 - 2026

Description

The Department of Materials Science and Engineering is engaged in a multi-year hiring effort for tenure-track professors whose interests are aligned with the School of Engineering and Applied Science’s strategic plan.

Applicants from all materials-related research areas are invited to apply, with particular interest in candidates who develop and/or use artificial intelligence, data science, and machine learning tools for new quantum materials discovery, synthesis, characterization, and applications with precisely controlled responses to external fields. Penn Engineering has signature initiatives in Data Science, Quantum devices and engineering (QUIEST) and Energy and Sustainability, as well as world-class facilities at the Singh Center for Nanotechnology and the Laboratory for Research on the Structure of Matter.

We seek individuals who are committed to nurturing and building our scholarly community in the broadest sense, participating in departmental, school-level, and university-level efforts towards this goal, and to work closely with students to identify and address challenges.

Penn Engineering strongly supports dual career couples, and we welcome and encourage inquiries about dual career assistance (for academic and non-academic opportunities) at an early stage of the recruitment process.

Quantum Materials Discovery, Synthesis, and Characterization for Advanced Applications: The discovery, development and characterization of new materials is crucial for advancing electronic, optoelectronic, energy and quantum technologies. It is anticipated that new, highly engineered materials will be synthesized with control over local atomic bonding, structure, ordering of defects, charges, spins, and polarization along with novel quantum geometries and topologies. We seek candidates with expertise in developing modern tools involving artificial intelligence, data science, and machine learning algorithms for new quantum materials discovery with precisely controlled responses to applied fields. Of particular interest are candidates who utilize AI/ML tools to predict and synthesize new materials, encompassing both bulk and low-dimensional materials, with applications in intelligent device platforms.

Areas of interest include but are not limited to:

• Autonomous and AI-driven approaches to materials discovery and optimization
• Data driven design and synthesis of bulk crystals, epitaxial growth of complex heterostructures, and thin film deposition techniques
• Scalable methods for producing low-dimensional materials
• Predicting and controlling material composition, structure, and properties at multiple length scales
• Bridging fundamental materials science with applications in electronics, photonics, and quantum engineering

The ideal candidate will demonstrate proficiency in predicting and controlling material composition, structure, and properties at multiple length scales to enable next-generation devices. Candidates who connect fundamental materials science to device performance are encouraged to apply, particularly those fostering interdisciplinary collaborations. Candidates should have a strong background in materials theory, synthesis, and/or characterization, with a vision for how their approaches can address current challenges in device performance and functionality while accelerating materials innovation.

Prof. Ritesh Agarwal chairs this search committee. Candidates are encouraged to apply early in order to be given full consideration. Deadline to receive applications is December 1, 2025.

Qualifications

Must have a Ph.D. in Materials Science and Engineering or related disciplines.

Application Instructions

• Applications must be submitted online via Interfolio. Applications include:
• Cover Letter
• Curriculum vitae
• Research statement (5-page limit)
• Teaching statement (2-page limit)