20 parallel-computing-numerical-methods-"Prof" Postdoctoral positions at University of South Carolina in United States
Sort by
Refine Your Search
-
, and Modeling: The duties will include collaboration between Prof. Tang’s lab and collaborators in other research laboratories. There will be periodic joint meetings to discuss collaborative research. It
-
and spectral line fitting, which could have been gained during the normal course of a doctoral degree program. Required to conduct business lawfully and ethically by consistently adhering to compliance
-
scale. Test methods for calibration corrections and noise reduction in retrieved data. Test algorithms for retrieval of sea surface temperatures from infrared radiances. Test algorithms for cloud masking
-
research groups in Chemistry and Materials Science; Science; The duties will include collaboration between Prof. Tang’s lab and collaborators in other research laboratories. There will be periodic joint
-
to at least the intermediate level of multiple regression Computer literacy with knowledge of at least one statistical software package (e.g., R, Mplus, Stata, SAS) Working collaboratively and effectively with
-
Doctoral Fellow Campus Columbia Work County Richland College/Division College of Engineering and Computing Department CEC Civil and Environmental Engineering Advertised Salary Range To commensurate with
-
parallel/GPU computing. Job Duties Job Duty Doing research problems in the area of mathematical foundations of data science and machine learning. The postdoc will assist with ongoing research projects
-
the research program. Use synthetic organic chemistry methods to develop and optimize fluorescent indicators and organelle-targeting ligands. Essential Function Yes Percentage of Time 35 Job Duty Design
-
, the individual will assist our research program, which examines the protein functions in neurons using Proteomics, AAV, and CRISPR genome editing approaches. Responsibilities will include assisting with primary
-
relevant programming languages. Ability to use/learn several advanced modeling methods (e.g., statistical, mathematical, individual-based, or machine learning models). Experience with high-performance