Piotr
Magierski Faculty of Physics, Warsaw University of Technology NUCLEAR THEORY GROUP ul. Koszykowa 75, PL-00-662 Warsaw, Poland position: Professor E-mail: piotr.magierski 'at' pw.edu.pl www: https://magierski.fizyka.pw.edu.pl/ Phone : 48 (22) 2345439 Fax: 48 (22) 6282171 also: Affiliate Professor of Physics Department (Nuclear Theory Group), University of Washington, Seattle, WA-98195 USA. Phone: 206-543-9754 E-mail: piotrm 'at' uw.edu www: https://phys.washington.edu/people/piotr-magierski |
Outline of research interests
My research interests are associated with the many-body quantum systems, such as atomic nuclei, atoms, molecules and atomic clusters. The great variety of quantum-mechanical phenomena that abound in these systems make them the unique laboratories for testing quantum mechanics, while their complexity poses an unprecedented challenge for the theory. |
In particular my research interests concern: Physics of cold atomic gases:
superfluidity, thermodynamics, exotic phases,
Bose-Einstein
Condensation (BEC), physics of BEC-BCS
crossover, dynamics of superfluid vortices, viscosity. Physics of atomic nuclei: structure of atomic
nuclei at extreme conditions: high angular momenta,
exotic deformations, large mass numbers, large neutron
to proton ratio. Neutron stars:
structure and stability issues, superfluid properties. Concerning general
theoretical issues related to quantum many body
systems: My research requires usage of the fastest supercomputers in the world. Currently I have access to |
Courses and information for students
USEFUL LINKS:
Pracownia Teorii Jądra Atomowego
Zakład Fizyki
Jądrowej Wydz. Fizyki PW
Nuclear Theory Group (University of Washington)
Cold Atoms Groups in the World
Quantum Many Body Nuclear Physics Group
Programme CompStar - The New Physics of Compact Stars (European Science Foundation)
Universal Nuclear Energy Density Functional (Scidac poster and popular article)
Quantiki - portal for everyone involved in quantum information science
Useful codes, Liquid Drop Formula
LINPRO: linear inverse problem library for data contaminated by statistical noise