86 parallel-and-distributed-computing-"LIST" Postdoctoral positions at Rutgers University
Sort by
Refine Your Search
-
communicate in English to sufficiently perform the job duties. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel and PowerPoint. Must
-
, and dissemination of results. The research program is focused on understanding prenatal determinants, such as extracellular vesicles and steroid hormones, that shape brain development and behavior
-
. Effective spoken and written English required. High level of computer literacy required. Experience in chromatography (including FPLC and preferably including HPLC) and electrophoresis required. Experience in
-
computational analysis of genome sequencing data in the Ellison laboratory. Position Status Full Time Posting Number 25FA1089 Posting Open Date 10/31/2025 Posting Close Date Qualifications Minimum Education and
-
of Neurosurgery is seeking a graduate of a MD or PhD program preferably with a background in basic sciences, particularly in skull-based Neurosurgery. This individual will work under the supervision of Dr Anil
-
. Posting Summary DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science, invites applications for postdoctoral associate positions for 2026-2028. The postdoc will be mentored by a
-
required. High level of computer literacy required. Preferred Qualifications In-depth understanding and hands-on experience in RNA-seq and ChIP-seq sample preparation, data collection, and data processing
-
. The training program is designed to impart the skills necessary for submitting successful career development awards. The emphasis on translational clinical research will require competitive applicants
-
including strongly correlated fermion materials, high-temperature superconductivity, topological electronic states of matter, developments and applications of computational methods at the density-functional
-
. The successful applicant will work in the areas of causal inference and statistical learning with high-dimensional observational data, including development of statistical and computational methods, and