Available research projects

The KU particle astrophysics group specializes in radiowave detection of neutrinos, specifically ultra-high energy neutrinos interacting in cold (Antarctic or Greenlandic) polar ice. Depending on their software background, students would work on either data analysis from existing experiments (ARA, ANITA, PUEO in Antarctica and/or RNO in Greenland) or perform design, simulation, construction and measurement of the radio-wave antennas and hardware used to perform measurements.

In this project, the student will work with real and/or simulated data from either the Deep Underground Neutrino Experiment (DUNE) or the Fermilab Short Baseline Neutrino (SBN) Program, two current and next-generation projects that aim at studying neutrino oscillation, that is the change in type or “flavor” as the elusive neutrinos travel. DUNE and SBND will study the behaviour of neutrinos over a broad range of energies and sources, from accelerator neutrinos to atmospheric and space neutrinos. The project will have both a physics analysis and an event reconstruction components. The first step will consist in performing an analysis task for one of the physics goals at DUNE or SBN, and identifying key metrics that affect the overall physics sensitivity. These result will then be used to investigate what areas of event reconstruction, the crucial step that interprets the DUNE and SBND liquid argon detector signals in terms of the particles emerging from neutrino interactions, directly influence these metrics. Depending on the student’s software skills, some targeted reconstruction algorithm development to boost the analysis physics reach is possible, including the use of machine learning techniques. Good proficiency with Python and/or C++ will be strongly preferred. This project has the potential to impact a future science mega-projects such as DUNE. The student will work as part of a team including a postdoctoral researcher and a couple of graduate students, and will likely have the opportunity to present their work to the broader experimental collaboration(s).

Prof. Ian Crossfield's ExoLab group is looking for a motivated student researcher to work with us on new studies of exoplanets and/or their host stars.  The project may focus on detecting new planets, modeling atmospheric chemistry of new worlds, observational characterization including transits and/or radial velocities, and determining physical properties of the planets and of their host stars. 

Beliefs about how physics knowledge is developed and assimilated naturally impact a student's learning experience in a physics course.  While interviews and surveys are typically used to assess this epistemology, these tools provide an incomplete assessment as the beliefs expressed in a survey or interview do not always align with actual practices.  We are addressing this limitation by examining student epistemology in introductory physics courses through analysis of self-prepared test aids (formula sheets, e.g.) in these courses.  Since a test aid is the product of an active and deliberate process of organizing knowledge, it provides evidence of knowledge structuring that is a more direct assessment of underlying epistemology than a response in an interview or survey.  An additional goal of this project is to determine how changes in epistemology correlate with changes in self-efficacy and/or mindset, as this will empower the development and evaluation of curriculum reforms and interventions targeting simultaneous improvement of these attributes.  Students participating in this project will use generative coding to characterize the test aids and subsequently determine the correlations between the epistemology determined from these test aids with the results of separate surveys of student self-efficacy and mindset in the same courses.  This analysis will enable a clearer understanding of the relationship between these attributes while also guiding the development and implementation of pedagogical reforms that target improvements in them.

Fundamental Technologies in concert with the University of Kansas Physics Department are seeking possible undergraduate students to support the ongoing activities and work alongside project scientists in support of data analysis and data production activities for several ongoing and former NASA missions. There are two aspects of this work that offer students the opportunity to get involved in the data work for these missions. 1) We are attempting to repackage existing data sets into more modern data formats that would enable more robust future scientific investigations; 2) We continue to analyze data from these missions in order to better understand the in-situ plasma environment and physical drivers that exists in the larger Heliosphere and/or targeted Planetary Magnetospheres.

Possible research and/or data projects includes working with data from the following missions:
1) The Voyager Interstellar Mission. A mission to study the outer Heliosphere as well as the
Very Local Interstellar Medium.
2) The Cassini Mission. A mission to study the Saturnian Magnetosphere
3) The Galileo Mission. A mission to study the Jovian Magnetosphere
4) The Van Allen Probes Mission. Dual spacecraft used to study Earth’s Magnetosphere
5) The Ulysses Mission. A Mission to study the Solar poles and Space Weather.
6) The ACE Mission. A Space Weather mission.
 

More information about the work on these missions can be found at: https://www.FTecs.com
The successful candidate would take on data production and/or data analysis role within one or
more of these projects and would have some flexibility one the nature of the scientific research
and/or the data science activities. The majority of the work is done off-campus (but still within a few
miles of the University of Kansas) and remote work is allowed once the candidate has undergone
successful on-boarding and shows a capability for independent work.

Note: Part of this work will be off-campus (but in Lawrence, KS), so only students that are planning to drive to KU should apply

A student working on this project will use infrared and millimeter data from the SOFIA and ALMA observatories to study tracers of the warm molecular gas toward the region surrounding the Milky Way’s central supermassive black hole.  The student will compare datasets covering high-temperature transitions of the abundant molecules H2 and CO in order to measure the properties of this gas, including its mass and temperature. Our ultimate goal is to determine the amount of warm molecular gas in the central parsecs of the Milky Way, in order to better constrain the inflow rate of gas toward the central supermassive black hole. The student will interpret the data to determine how much of the visible gas is truly located in close proximity to the black hole, and how much of it is due to line-of-sight contamination. This work  will help validate theories of the 3D structure of the Milky Way center as well as better understand the accretion flows that are responsible for growing the supermassive black holes at the centers of galaxies.

Lithium-ion batteries, with their high-energy density, high-discharge voltage, and relatively low cost, have been the battery of choice for a wide variety of applications, including portable consumer electronics, hybrid- and all-electric cars, and grid-scale energy storage. However, these batteries also come with drawbacks: potential safety issues and growing concerns regarding the availability of lithium and of the cathode materials. Replacing lithium ions with earth-abundant, non-toxic, and non-flammable ions would alleviate these concerns. 

We will use computational methods that start from only the atomic structure (so called first-principles methods), based on density functional theory, in combination with machine learning to find and investigate new cathode materials without having to rely on time-consuming experiments.

Bio: Dr. Peelaers is a computational condensed matter physicist who uses and develops first-principles methods, based on density functional theory to improve our understanding of the physics of nanostructured, electronic, and energy materials, with a focus on wide-bandgap oxides. His work aims to design and improve the next generation of power electronics, sensors, memristors, and batteries.

Phase change materials (PCMs) is one of the key materials platforms for next-generation high-density storage, in-memory and neuromorphic computing. These technologies are essential for meeting the growing demand for computing and storage at significantly reduced energy consumption. The performance of PCMs can be optimized by tuning and controlling the structure and properties at the interfaces. In this project, the student will engage in computational research on PCMs using quantum-mechanical first-principles calculations and machine learning techniques. The goal is to explore and design novel interface structures and study their properties. Students with Python programming skills will be preferred.

The student will design, construct, and test a laser heating apparatus that will be integrated with an inkjet printer for printing high-performance semiconductor quantum dots/graphene photodetectors. The goal is to demonstrate on-chip programmable printing of high-performance electronic and optoelectronic devices with minimum thermal budget using focused laser heating coordinated with printing. The result is directly related to future electronics integrating nanostructured devices with CMOS. The student(s) will determine what the best approach is and implement it accordingly. The student(s) is expected to design and integrate a variety of mechanical, optical and electronic to make the apparatus functional and user friendly.

Memristors can be used as artificial neurons and synapses in neuromorphic circuits for artificial intelligence, computing, etc with high speed and energy efficiency. In this project, the student will team up with graduate students to investigate dynamic properties of memristive switches using scanning tunneling spectroscopy (STS). This involves developing methods to process and visualize STS data, identifying features related to memristor behavior, and correlating them with electrical switching events. The student will explore approaches to optimize data analysis workflows and refine protocols for high-resolution spectroscopy of memristor dynamics. The goal is to understand how nanoscale electronic changes influence device performance and endurance. The results are directly related to advancing the design of next-generation electronic memory and neuromorphic computing devices.