Sort by
Refine Your Search
-
environmental studies - to make novel insights and to tackle the full complexity of human health. We seek applicants to join any of the several interdisciplinary research projects within Princeton Precision
-
position for new projects to characterize synthesis processes and novel materials in several research thrusts: i) development of advanced manufacturing processes for low-cost battery cathode active materials
-
: Responsibilities *Explore, collect, and preprocess various sources to develop domain LLM training and test datasets *Design and implement fine tuning and RAG workflows for LLMs on a variety of datasets *Maintain
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
-
. Major project goals will include: inferring the most comprehensive squamate evolutionary tree to date; its integration with fossil data to produce accurate divergence time estimation for all major groups
-
technologies. The Pritykin lab (http://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi
-
applications to alternative fuel design and atmospheric chemistry. The successful candidate will be expected to assist with the commissioning of a new shock tube facility and will conduct fundamental
-
Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
for this position will work to develop a conservative machine-learning based sea ice model correction that can be applied to fully coupled climate model simulations. The project will involve: 1) the development of a
-
September 2025. The Ferris group studies high-temperature reaction chemistry and particulate formation using optical diagnostic methods, with applications to alternative fuel design and atmospheric chemistry
-
interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials