Introduction: The Hoopfer Lab studies the neural circuit basis of behavior in Drosophila (fruit flies), with current work focused on how self-grooming sequences are shaped by social, environmental, and genetic factors. Alterations in these behavioral sequences are associated with various neuropsychiatric disorders, making them a valuable model for understanding disrupted action patterns. While prior studies have relied on standard statistical approaches to characterize grooming behavior, more recent work (Mueller et. al 2022) has found structured patterns in grooming sequences that merit deeper computational analysis. At the same time, the ability to analyze dynamical systems via these sequential patterns is part of the toolkit of Pattanayak group’s research on dynamical systems focusing on understanding information entropy and complexity in these contexts. We are seeking a research assistant to contribute to this interdisciplinary collaboration between neuroscience and physics. The project will involve both theoretical development and implementation of analytical methods to investigate fly grooming sequences.
Who should apply: Students majoring in physics, biology, computer science, cognitive science, statistics, and mathematics are encouraged to apply.
Required Skills: Experience in mathematics, including a basic understanding of probability. Experience with Python is preferred, though motivated students willing to learn quickly (with guidance from graduating senior lab members) are encouraged to apply. You will work with existing code that will be modified and extended as needed. Familiarity with standard scientific Python libraries such as NumPy, Scikit-learn, and Matplotlib is helpful, or a willingness to learn them as needed. Some experience with (or enthusiasm for) nonparametric statistics, machine learning, natural language processing, dynamical systems, statistical inference, or information theory is preferred.
Apply If You Enjoyed: MATH240: Probability, STAT250: Statistical Inference, STAT330: Advanced Statistical Modeling, CS320: Machine Learning, CS322: Natural Language Processing, or MATH232: Linear Algebra
Timeline & Commitment: The position will begin in the second half of Spring term and is expected to continue into the following academic year through the Directed Research course (for credit). There is also the possibility of an 8-week summer research internship.Applications: Please complete the application form by Friday, April 24.