Survey of thermodynamics. Boltzmann distribution, statistical interpretation of thermodynamics, Gibbsian ensembles and the method of Darwin, Fowler; quantum distributions and their applications, transport phenomena. Prerequisites: PHYSICS 424, 601, 606 (the latter may be taken concurrently).

Lagrange's and Hamilton's equations, central force problem, rigid bodies, small oscillations, continuum mechanics, fluid dynamics. Prerequisites: PHYSICS 421, 422.

Special projects; departmental sponsor required. Prerequisite: one or more 400-level physics courses.

Mathematical and conceptual aspects of the special and general theories of relativity. Topics include: Lorentz transformations, convariant formulation of the laws of nature, the equivalence principle, curved spaces, and solutions of the equations of relativity.

Description of electric and magnetic fields in a dynamical context-electromagnetic radiation theory, optics, plasma physics, relativistic electrodynamics, cavity resonators, waveguides. Prerequisite: PHYSICS 422.

This course, intended for students with a background in physical, chemical, or quantitative life science, will cover physical principles that apply to biological molecules and cells: Bownian motion, low Reynolds-number environments, forces relevant to cells and molecules, chemical potentials and free energies, as well as the basics of polymer physics. The emphasis will be on theory and model-building; specific topics will include molecular machines, self-assembly, membranes, DNA, and RNA.

This course introduces the fundamental concepts and techniques relevant for physical implementations of quantum computing. The course focuses on continuous quantum dynamics of few-qubit systems, using concrete examples of superconducting qubits as a main thread. It is intended for physics, engineering, and computer science juniors and seniors who wish to learn the language of experimental quantum information research. (It is particularly suited as a sequel to COMPSCI 490Q: Quantum Information Science from a hardware perspective).

The course will provide an introduction to statistical data analysis and statistical learning. The student will learn both fundamental concepts of frequentist and Bayesian data analysis as well as practical applications using Python. Machine learning applied to data analysis will be discussed.

Operation and use of the basic elements of modern electronics. Circuits designed and constructed in lab are tested using modern instrumentation. Principles of analog circuit analysis, filters, diodes, transistors, operational amplifiers, oscillators, power supplies, and integrated circuits. A hands-on experience for those using electronic equipment in research, testing, and analysis. Recommended for majors in all pure and applied sciences. Prerequisite: PHYSICS 286 or equivalent sophomore physics lab experience.

Operation and use of the basic elements of modern electronics. Circuits designed and constructed in lab are tested using modern instrumentation. Principles of analog circuit analysis, filters, diodes, transistors, operational amplifiers, oscillators, power supplies, and integrated circuits. A hands-on experience for those using electronic equipment in research, testing, and analysis. Recommended for majors in all pure and applied sciences. Prerequisite: PHYSICS 286 or equivalent sophomore physics lab experience.