UC Merced

Name: David Strubbe
Title: First principles calculations of doped 2D materials
Description: Our condensed-matter theory group uses quantum mechanics and high-performance computing to simulate the properties of materials. A particular interest is 2D materials, which have atomically thin layers which are bonded in plane and have Van der Waals interactions between layers. Examples are graphite/graphene, black phosphorus, transition-metal dichalcogenides (e.g. MoS2), hexagonal boron nitride, and III-VI materials (e.g. GaTe). These materials have exciting optoelectronic applications such as transistors, sensors, photovoltaics, light-emitting diodes, lasers, spintronics, and quantum computing, using their unique quantum properties. 2D materials can adopt various crystal structures and can be doped with other atoms (substitutionally, or between the layers). The Strubbe group uses density-functional theory (DFT), for structure and electronic properties, and density-functional perturbation theory (DFPT), for phonons, to study these materials. The student can perform their own study of a particular material structure within this family, following the pattern of one of our recent studies of Ni- and Re-doped MoS2. They will learn about searching materials informatics databases and constructing and visualizing crystal structures. They will run open-source DFT codes such as Quantum ESPRESSO and Octopus, learning about parallel computing on a cluster, analyzing data such as bandstructures and Raman spectra, and using calculations to parametrize simple models such as the Ising model for magnetism. Depending on student interest and skills, they could also undertake some programming work to add new capabilities to the code.
Preferred qualifications: Basic knowledge of quantum mechanics (e.g. "modern physics" level), basic knowledge of programming


Name: Xiaoyi Lu
Title: Building an AI-enabled Ag Tech Simulation System with Digital Twin
Description: Our aim is to build an AI-enabled Ag Tech Simulation System with Digital Twin technologies. This project will leverage real-world data from the UC Merced Experimental Smart Farm (ESF) for faster experiment execution and data collection. The system allows parallel experimentation that may be economically or logistically challenging in a real farm setting. While bespoke simulations are initially costly and complex to develop, creating a shared foundation reduces the deployment effort for researchers. This enables them to focus on customizing the platform and conducting productive research, with the potential for reuse by others, further streamlining system utilization.
Join us in our exciting project to develop this new AI-enabled system with modern Digital Twin technologies. This unique opportunity allows you to work with real data and conduct experiments that were previously challenging. By working with us, you can explore cutting-edge research in agriculture, AI, distributed computing, and digital twin technology. Join our team of innovative researchers and contribute to the future of smart farming. Elevate your research career by becoming part of this dynamic project.
Preferred qualifications: Python/C/C++, Unity/WebGL/Other similar tools