See what it's like to be a PharmD student at URI College of Pharmacy.....attend a class with a current P3 PharmD student and take a tour of Avedisian Hall! Visiting students will have the opportunity to shadow a Pediatric/Geriatric Pharmacotherapy course with a currently enrolled student during the date/time noted. Note for admissions: Allow 3 students per block (Parents can attend prior to 2 pm class, but only students are permitted to shadow the class)

This webinar explores the way to look for public data, taking the evolution of the U.S. civil service system. It is to learn how current US executive action, which aims to remove civil service protections, can be understood as political rather than evidence-based. Modern civil service principles-merit-based hiring, performance-based promotion, and protection from political reprisal-are designed to ensure expertise and efficiency while insulating administration from political pressures. However, the current proposal has reopened debates about the trade-offs between efficiency, accountability, and democratic representation. This webinar highlights how we can ensure that misinformation can be discredited by public data if you know how to do so.This webinar is brought to you by Nippon Foundation Ocean Nexus at the University of Rhode Island and The Daniel J. Evans School of Public Policy and Governance at the University of Washington.

it-tls@uri.edu is inviting you to a scheduled Zoom meeting.Join Zoom Meeting https://uri-edu.zoom.us/j/92280383907?pwd=9oVmzB3HaXGAZhakO3m2yIMDarB2Ii.1Meeting ID: 922 8038 3907 Passcode: 912345Join instructions https://uri-edu.zoom.us/meetings/92280383907/invitations?signature=DOK7I1_SMVd6HtrM5AvPNYUDkv2WzK8Ab2XbvMCpD60

Collaboration with iStand & VPAS to further educate the URI community on sexual violence prevention and advocate for a safer campusVisit URInvolved for complete event details.

Have you ever considered studying in Spain, Ireland, Costa Rica, or South Korea for either a short term or full semester program?Rhody Abroad Programs are highly supported, more affordable study abroad programs. With proper planning most majors can find a Rhody Abroad option that will keep you on track for graduation.Want to learn more about studying on one of these programs or how to get started? Join us for this information session.

The Institute for AI & Computational Research is continuing the series of talks on AI/ML techniques and applications across various scientific domains. We envision the talks to strike a good balance between depth and breath. The goal of these talks will be to (i) introduce the particular AI/ML technique to fellow faculty and graduate students who have a basic understanding of deep learning and (ii) present a variety of applications in different domains without assuming deep domain knowledge. Each event will start with a 45 minute talk with 15 minutes for questions and a subsequent 30 minute networking session for brainstorming and further discussion. The speakers will be faculty from a number of URI colleges or nearby universities.We are kicking off our fall seminar series with the talk below and are looking forward to see many of you at the seminar!Speaker: Scott Field (Umass Dartmouth) Date/Time/Location: Sept 18, 3pm, Memorial Union room 308. Title: Physics-Informed Machine Learning for Modeling Orbital Dynamics in Binary Black Hole SystemsAbstract: Einstein’s field equations of general relativity are among the most challenging differential equations in physics. They predict that two black holes can collide, producing powerful distortions of spacetime—gravitational waves. The first direct detection of these waves in 2016 marked a historic milestone, recognized by a Nobel Prize. In this talk, I will introduce the problem of binary black hole collisions and the role of gravitational waves. These waves can be studied either by numerically solving Einstein’s equations (the forward problem) or by analyzing experimental detections to infer their sources (the inverse problem). I will present a machine learning framework that discovers the governing dynamical model of binary black hole motion. Our approach begins with universal ordinary differential equations parameterized by feed-forward neural networks, and employs physics-informed constrained optimization to explore a space of plausible models. Applied to a variety of systems, this method enables forward extrapolation and automatically uncovers relativistic effects such as perihelion precession and radiation reaction. I will close by highlighting challenges and opportunities for extending these ideas to other areas of science and engineering.