483 postdoc-in-thermal-network-of-the-physical-building positions at Princeton University
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
"Post-doctoral Associate in Theoretical High-Energy Physics'' The Physics Department at Princeton University expects to have post-doctoral or more senior research positions in Theoretical High
-
building infrastructure and resources to propel discovery, development and dissemination in translational research across New Jersey. Reporting to the Principal Investigator of the grant at Princeton
-
project will design and build magnet component prototypes and test probes to demonstrate and validate integrated high field measurability for physics and fusion materials science research. The use of high
-
ultrahigh vacuum (UHV) surface science facilities enable: (i) measurements of surface composition, structure, and thermal stability using AES, LEIS, XPS, and vibrational spectroscopy; (ii) quantitative
-
. The lab manager will serve as a project manager for projects ongoing among postdocs and students, and will design experiments and undertake method development. The lab manager will also be responsible
-
goal of helping to accomplish the NJ ACTS mission - training the next generation of researchers and building infrastructure and resources to propel discovery, development and dissemination in
-
innovation at the intersection of Engineering and the Life Sciences. ODBI is evolving rapidly, including launching various programs and building physical and programmatic infrastructure. ODBI is seeking a
-
and mouse colony management. This is a one-year appointment with the possibility of extension. The lab manager will serve as a project manager for projects ongoing among postdocs and students, and will
-
-frequency electronics, thermal transport physics, and beamline experiments at user facilities. Besides technical expertise, we value strong communication, teamwork, and mentorship. Candidates should be able
-
Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757