162 postdoc-density-functional-theory-dft Postdoctoral positions at Princeton University
Sort by
Refine Your Search
-
://puwebp.princeton.edu/AcadHire/position/36402 and submit a cover letter, CV, a research statement that includes your specific plans and goals for advancing equity and inclusion if hired as a Princeton postdoc, and
-
simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic acids, ligands), coarse-grain and polymer model development
-
differential equations, computational fluid dynamics, material science, dynamical systems, numerical analysis, stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
-
, anticipates offering a number of postdoctoral or more senior research positions in theory, observation and instrumentation, including (but not limited to): the Lyman Spitzer, Jr. Postdoctoral Fellowship
-
some of the following areas: molecular dynamics, Monte Carlo simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic
-
, and protein engineering. The group is part of the Department of Chemical and Biological Engineering (https://cbe.princeton.edu/). Qualified candidates will be highly motivated, with a strong
-
background check. The work location for this position is in-person on campus at Princeton University. Appointments are for one year with the possibility of renewal pending satisfactory performance and
-
Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
-
to maximize their potential to explore, discover and understand emergent behavior of complex quantum matter. The Moore Postdoctoral Scholars in Theory of Quantum Materials program is an integral part of EPiQS
-
boundary layer and apply them to ocean climate models. Our previous work demonstrated that neural networks can learn to predict the vertical structure of vertical diffusivity and the networks can then be