73 algorithm-development-"Multiple"-"Prof"-"Prof"-"Newcastle-University" Postdoctoral positions at Princeton University
Sort by
Refine Your Search
-
; biodiversity; conservation; environmental science and policy; infectious disease and global health; and sustainable development in impoverished and resource-challenged regions of the world. The Term
-
, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
-
research positions in the following fields:1. Microfluidic and Lab-on-Chip development in a multidisciplinary lab. Candidates should demonstrate track-records in microfluidics, Lab-on-Chip, and micro
-
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
-
Department, an innovative, collaborative, and vibrant research environment. Princeton University is in an idyllic college town halfway between New York City and Philadelphia, with convenient train access
-
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
-
until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus
-
Department, an innovative, collaborative, and vibrant research environment. Princeton University is in an idyllic college town halfway between New York City and Philadelphia, with convenient train access
-
researchers working on an NIH funded project focused on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to
-
discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion