138 parallel-computing-numerical-methods Postdoctoral research jobs at Princeton University
Sort by
Refine Your Search
-
Postdoctoral Research Associates in a parallel job posting. The Postdoctoral Research Associates will be appointed through the Center for Statistics and Machine Learning with the possibility for affiliation with
-
-to-Decadal Variability & Predictability Division, Technical Services and Modeling Systems Division. The selected candidate will have access to state-of-the-art numerical models and high-performance computing
-
include: a Ph.D. in Neuroscience, Psychology, Cognitive Science, Computer Science, Engineering, or other related field, and strong experience with computational models, programming, and quantitative methods
-
, such as survey and sampling design and data analysis (in R or Python), meta-analysis and/or document/text analysis, or computational modeling *An interest in mixed-methods approaches, including also
-
The Department of Electrical and Computer Engineering has opening for postdoctoral research positions in the following fields: 1. Microfluidic and Lab-on-Chip development in a multidisciplinary lab
-
The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior researcher to work on projects related to computational analysis of chemical and biochemical datasets
-
-rated accordingly. The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.
-
] Subject Areas: Machine Learning / Machine Learning Analytical Chemistry / Current Advances in Chemistry & Biochemistry Computational Science and Engineering / Machine Learning Artificial Intelligence
-
quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation
-
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