Survey of electrodynamics, focusing on applications to research done in the Department. Topics include mathematical structure and relativistic invariance properties of Maxwell equations, tensor methods, and the generation and scattering of radiation, in vacuum and in materials. Applications vary from year to year but include optical manipulation, astrophysical phenomena, and the generalizations from Maxwell's theory to those of other fundamental interactions (strong, electroweak, and gravitational forces).

Introduction to physical cosmology emphasizing recent ideas on the very early evolution of the universe. The course will introduce standard big bang cosmology, new theories of the very early universe, and the key observations that have tested and will be testing these ideas. No prior knowledge of astrophysics, cosmology, general relativity, or particle physics will be assumed, although aspects of each will be introduced as part of the course. The course is intended for graduate students and advanced undergraduates.

Introduction to research in particle, nuclear, condensed matter and astrophysics. Selected current topics from journals. Prerequsite: Taken by all first-year graduate students. This is a required seminar that does not carry or a grade.

Interaction of light with matter. Traditional imaging and polarization optics. Interference, diffraction, coherence, absorption, dispersion, spectroscopy, stimulated emission, introduction to lasers and non-linear processes.

In this course you will have the opportunity to do a variety of experiments, ranging from "classic experiments" such as measuring G with a torsion balance, determining the relativistic mass of the electron, and muon lifetime, to experiments studying atomic spectroscopy, NMR, Optical pumping, Mossbauer effect, nuclear energy levels, interaction of gamma rays with matter, single photon interference, and magnetic susceptibility. There are also experiments using a High-Tc superconducting tunnel junction and a PET scanner. You will learn basic statistics, become proficient in analysis using Python, acquire an understanding of systematic errors, and learn how to write a professional report. Many of the laboratories provide excellent opportunities to exercise, and expand upon, the knowledge you have gained in your physics courses.

Perturbation theory, variational principle, application of the quantum theory to atomic, molecular, and nuclear systems, and their interaction with radiation.

A laboratory-intensive survey of analog and digital electronics, intended to teach students of physics or related fields enough electronics to be effective in experimental research and to be comfortable learning additional topics from reference textbooks. Analog topics include voltage dividers, impedance, filters, operational amplifier circuits, and transistor circuits. Digital topics may include logic gates, finite-state machines, programmable logic devices, digital-to-analog and analog-to-digital conversion, and microcomputer concepts. Recommended for students planning to do experimental work in physical science. Prerequisite: Familiarity with electricity and magnetism at the level of PHYS 102, 141, 151, 171.

Second term course in intermediate electromagnetism. Topics include magnetostatic forces and fields, magnetized media, Maxwell's equations, Poynting and stress theorems, free field solutions to Maxwell's equations, and radiation from separable and nonseparable time dependent charge and current distributions.

This is a course on data analysis and statistical inference for the natural sciences focused on machine learning techniques. The main topics are: Review of modern statistics, including probability distribution functions and their moments, conditional distributions and Bayes' theorem, parameter estimation, Markov chains; Fundamentals of machine learning, including training/validation samples, cross-validation, supervised vs. unsupervised learning, regularization and resampling methods, tree-based methods, support vector machines, neural networks, deep learning and image analysis with convolutional neural networks. Students will obtain both the theoretical background in data analysis and get hands-on experience analyzing real scientific data. This course forms a two-course sequence with Phys 358. Students must also have prior programming experience in python.