There are plenty of opportunities for geology and other science students to conduct research over the summer.
Integrated Stratigraphic and Paleoenvironmental Study of the Middle-Late Devonian Carbonate to Black Shale Transition in the Michigan Basin
Professor Jay Zambito is leading a project that aims to decipher carbon cycle and sedimentological changes that occurred during the Middle-Late Devonian (~375 million years ago) as a result of global climate change. This project is part of the Keck Geology Consortium, and summer research positions are tailored to rising seniors that plan to use the data they collect for their geology senior theses.
Devonian climate trends have long been studied within the context of biological change. For example, the End-Devonian glaciation is typically thought to be related to CO2-drawdown due to the evolution of forests during the Middle-Late Devonian. However, more recent paleoclimate reconstructions indicate that the Devonian climate story is more complex. In order to better understand these long-term patterns, a current focus within the Devonian research community is the study of the repeated, short-duration, globally-recognized events that resulted in marine anoxia, extinctions, and carbon-cycle perturbations. This project is focused on recognizing these events, and reconstructing the environmental change associated with them, in the Middle through Late Devonian strata of the Michigan Basin.
Summer Science Research
Each summer, there are opportunities for Beloit College students to work with science faculty on their research or through the Pakula Biomedical Fellowship Program. These opportunities are either 4 weeks or 8 weeks in length. Students receive college credit and a stipend for their work. All participating students live on campus.
The Summer 2025 research opportunities are listed below. All of these projects are part of the Pakula Biomedical Fellowship Program. Please note the length of the projects: 4 weeks (May 19 - June 13, 2025) and 8 weeks (May 19 - July 11, 2025). Students will work with a faculty mentor to develop a research question and participate in the weekly 2-hr professional development seminar (0.5-1 unit of special project). Fellows will receive a stipend of $2750 (4 weeks) or $5500 (8 weeks), to help offset the costs of summer tuition, and room and board. To review the available projects, click on the topics below. For more details, contact the principal investigator for each project directly. For general questions about the Pakula Biomedical Fellowship program, contact Dr. Tawnya Cary (caryt@cnyc86.com).
Students interested in working in other STEM disciplines should check the respective discipline website or contact individual faculty members.
Projects
This project is part of the Pakula Biomedical Fellowship Program.
Forensic investigators have begun to use technologies such as Computed Tomography (CT) studies to assess Bone Mineral Density (BMD) differences amongst age, sex, and pathology of decedents under their care. This research has the potential to help greatly with the identification of unknown decedents. However, the integration of these studies with archaeological and microbiological research regarding the changes of a body during the Post-Mortem Interval (PMI) and across levels of decomposition has been lacking. More knowledge regarding bone density changes after death is necessary in order for the use of BMD data to be applied appropriately to unknown decedents. This project will use the CT studies from a consenting donor database to measure the BMD of decedents at different levels of decomposition.
Focus Areas: Biology
Project Duration: 8 weeks (05/19/25 - 07/11/25)
Prerequisite Courses: BIOL 256, ANTH 230, or HEAL 301
This project is part of the Pakula Biomedical Fellowship Program.
A common general chemistry lab experiment is aspirin synthesis. Students are introduced to synthetic techniques through the synthesis of aspirin (acetylsalicylic acid) from salicylic acid and acetic anhydride. This lab is often a favorite of students, but how was aspirin developed? Salicin, a neighbor to aspirin, is a natural product derived from willow bark. Salicin has analgesic properties that have been utilized by indigenous communities for millennia. Knowledge of salicin lead to experimentation that resulted in aspirin. Acknowledging and highlighting the knowledge of indigenous people is an important part of contextualizing the scientific world. This projects seeks to expand the current aspirin synthesis project to include indigenous science. Student researchers will develop a plan for salicin extraction from willow bark, optimize a the procedure, analyze the product, and work collaboratively to develop content goals and activities. Student researchers will gain experience with experiment design and optimization, literature surveys, analytical techniques, and educational content creation.
This project is part of the Pakula Biomedical Fellowship Program.
In this 8-week summer research experience, students will use mathematical tools to explore epidemiological models of emerging diseases. By applying differential equations, statistical methods, and computational simulations, participants will analyze how diseases spread, assess intervention strategies, and investigate factors influencing outbreaks.
Students will work collaboratively to develop and analyze models, simulate disease transmission, and interpret real-world data. Projects may involve studying vaccination strategies, contact tracing, or the impact of public health policies. This research is critical for improving our ability to predict and control disease outbreaks, directly impacting public health decision-making.
Participants should have completed coursework in calculus and introductory statistics. Experience with differential equations or programming (e.g., Python, R, or MATLAB) is helpful but not required. This program is an excellent opportunity for students interested in applying mathematics to real-world challenges in epidemiology and public health.
This project is part of the Pakula Biomedical Fellowship Program.
Flowering plant reproduction poses challenges for restorationists and gardeners. Flowers may require specific pollination conditions; for example, tomatoes must be “buzz pollinated” by a bumblebee or a tuning fork. Resulting seeds often remain dormant until conditions are right to germinate (sprout). A third problem is the “phenotyping bottleneck” of effort required to manually observe traits. Computer tools can improve this by quantifying features present in images.
I propose a project to: -Create a workflow to count the total number of seeds and the number of successfully germinating seeds in a controlled environment. This could be done using either ImageJ or the Python-based SeedGerm software. -Use this workflow to count the number of viable seeds in an existing pilot sample of 180 tomatoes, some of which are the result of buzz pollination -Expand this workflow to investigate techniques for improving germination in native wild plant seeds with low germination rates such as Wild White Indigo (Baptisia alba), or Wild Bergamot (Monarda fistulosa). This could include cold treatment and/or scarification.
Focus Areas: Biology
Project Duration: 8 weeks (05/19/25-07/11/25)
Prerequisite Courses: At least one intro biology course
Preferred Courses: BIOL 121 and experience in computer science
This project is part of the Pakula Biomedical Fellowship Program.
In this project, students will work with data from electroencephalograms (EEG) of mice and humans with epilepsy to detect, quantify, and evaluate the presence and absence of seizures and other epilepsy-related signal types. This work is critical to understanding the evolution and prevention of epilepsy in response to traumatic brain injury. Students will work in Python or Matlab to write and evaluate scripts that apply previously published and new algorithms to EEG data for this purpose. Prior experience with Python and/or Matlab is required. Coursework related to Data Science is helpful but not required.
This project is part of the Pakula Biomedical Fellowship Program.
Perdition of chronic diseases such as chronic kidney disease, heart disease, Hepatitis, and other disease at early stages is considered to be critical to manage and control it. Several techniques in machine learning (ML) and data mining can be employed to predict the presence of chronic diseases. The main goal of this research is to develop a model based on traditional ML and data mining such as Decision Trees, Support Vector Machines, Artificial Neural networks and Bayesian classifiers in order to predict the presence of some of the chronic disease and to assist physicians in maximizing accuracy for identification of these diseases’ severity stage. In addition, we will also employ deep learning techniques (such as CNN, DNN, and RNN), Transfer Learning (TL), Federated Learning (FL), Meta learning, and compare their performance with the traditional ML techniques.
