Graduate students

Our group is looking to welcome 2-3 new Ph.D. students each year. We welcome applicants from a range of disciplines, including Electrical Engineering, Materials Science, Mechanical Engineering, Physics (or Applied Physics), Computer Engineering, Chemical Engineering, and Chemistry. If our research aligns with your interests and expertise, we encourage you to reach out. See the Areas of Interest below. Prior experience with materials deposition/growth and/or electronic device fabrication and measurements is highly preferred.

To apply: If you are applying to UC Berkeley EECS in Fall, please select “Asir Intisar Khan” as one of your top faculty of interest during the Fall PhD application cycle. If you are applying through a non-EECS program, please mention my name in your Statement of Purpose, and email me to let me know what department you are applying to.

If you are interested in joining our group, please email us at asir@berkeley.edu. In your email, start the subject line with: “Prospective PhD Applicant”. Briefly describe what you would like to work on, and explain how you would extend or improve on one of the areas of your interest that aligns with us. Attach your CV as a PDF. If you’ve worked with any of our collaborators or have relevant industry or research experience, mention that in your email as well.

Areas of Interest:

(i) Nanofabrication and characterization of emerging memory devices (ii) Wide and Ultrawide Bandgap Device fabrication and measurement (iii) Ferroelectric heterostructure deposition, metrology, and transport characterization. (iii) Nano and micro-scale heat transfer, thermal characterization, heat management, and nanofabrication, (iv) Synthesis, electronic transport, and theoretical modeling of topological materials.

Our group will focus on the development of low-power electronic devices by engineering heterogeneous device platforms and transformative materials, with a particular emphasis on controlling charge, heat, and spin transport at their interfaces. We are especially excited to contribute to next-generation AI hardware on both rigid and flexible substrates, and to explore novel interconnects and new thermal strategies for energy-efficient, 3D-integrated electronic systems. We are also interested in leveraging machine learning for accelerated design of experiments, materials discovery, and physics-informed modeling, to enhance our understanding and optimization of materials and devices at multiple scales. These efforts and innovations will complement software and circuit design efforts, driving collaboration and multiplying energy-efficiency gains across computing hardware.