Sort by
Refine Your Search
-
genome data available. Similarly, the NOAA Geophysical Fluid Dynamics Laboratory (GFDL) has world-leading expertise in climate modeling and access to valuable climate, weather, and air quality data
-
on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to advance the use of mathematical and statistical models
-
C.V. online. Upon request, candidates should be prepared to submit references, code and/or writing samples, and transcripts. The final candidate will be required to complete a background check. The work
-
sample (15 pages or an excerpt); and contact information for three references. Please see current course descriptions for models (https://writing.princeton.edu/undergraduates/writing-seminars
-
. The scope of the work builds on recent publications from the laboratory, e.g., predicting future illicit drugs with chemical language models (https://www.nature.com/articles/s42256-021-00407-x) and re
-
multimessenger analyses. Candidates with experience in gravitational-wave data analysis, source modeling, and electromagnetic follow-up are all encouraged to apply. Princeton hosts a strong research program in
-
incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes occurring at plasma-material interfaces in fusion
-
of interest include: Metabolomics, isotope tracing, metabolic flux analysis, quantitative modeling, mass spectrometry imaging, cancer metabolism, small molecule inhibitor discovery, dietary impact on cancer
-
of the work builds on recent publications from the laboratory, e.g. integrating language models with mass spectrometry data (https://www.nature.com/articles/s42256-021-00407-x, https://www.nature.com/articles
-
modeling expertise across this interdisciplinary research space at Princeton. Data scientists should have a strong track record in modeling big data using advanced deep-learning methods. A prior experience