REU projects
Project Categories
Available Project Titles, Mentors, and Abstracts
The REU student will help modify the Python code for the UF3 machine-learning method to extend the learning algorithm and apply the method to study ionic and magnetic materials. The research results in the form of the software will be made freely available to the community through our Github community site.
The REU student will formulate a 3D culture medium made from a mixture of microscopic hydrogel particles and liquid growth media. Time-lapse fluorescence microscopy will be performed to investigate the coupling between cells. The correlation between fluctuations in cell shape and in intracellular calcium will be studied in time and across distances between neighboring cells.
The REU student will use a suite of pre-existing computer programs to calculate properties in different quasicrystalline hosts. One focus will be a search for different ground states separated by phase transitions occurring at the absolute zero of temperature. The student will acquire understanding of key concepts in contemporary condensed matter physics, while gaining experience of planning, running, and interpreting large-scale simulations in a Linux environment.
Project Titles, Mentors, and Abstracts (already assigned)
Working with a postdoctoral scholar and faculty members in the MagLab High B/T Group, the REU student will measure the surface area of sintered metallic particles using the BET method. The student will learn to handle liquid nitrogen, configure vacuum equipment, and use computer software (e.g. Labview, Solidworks, COMSOL) to acquire and analyze the data.
REU students will synthesize high entropy alloy samples via arc melting. They will then characterize the samples by x-ray diffraction and SQUID magnetometry. Students will learn basic python data analysis and plotting techniques and use these skills to analyze there data.
The REU student will apply state-of-the-art orbit modeling codes to precision astrometric data, learning how to analyze the kinematic coherence of stellar structures through a combination of statistical and dynamical techniques.
The REU student will grow thin films of SrIrO3 using pulsed laser deposition (PLD) and characterize the magnetotransport properties of these thin films. The student will be trained to use a PLD system and measure the low temperature magnetoresistance and the Hall effect.
The REU student will use this existing three dimensional simulation software after first writing a simplified one dimensional version themselves to understand the physics involved. Depending on the background of the student, they can modify the underlying code or primarily use a Jupyter or Matlab notebook to run the code. The REU student will have the opportunity to use the U.F. HiPerGator supercomputer and to use machine learning techniques to help optimize the morphology for maximum power output for a given set of materials parameters.
The REU student will learn the fabrication techniques for 2D heterostructure devices and basic characterization techniques like Raman, absorption and photoluminescence spectroscopies that will be useful in assessing the device quality. Part of the project will also involve writing LabView code to automate the fabrication and characterization procedures.
The REU student will learn to build artificial neural networks to process images of specimens from the local natural history museum. There will also be opportunities to take images of diverse museum collections using a hyperspectral imaging setup, and to make physical models of artificial images using custom reflective materials.