78 parallel-and-distributed-computing-phd Postdoctoral positions at Rutgers University
Sort by
Refine Your Search
-
. 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
-
functioning of their biomedical research laboratory, and to fulfill the research and teaching requirements of the Rutgers IRACDA, INSPIRE Postdoctoral Training Program. Among the key duties of this position
-
starting on, or after, 7/01/2026. The position is to be offered jointly with the Center for Computational Astrophysics (CCA) at the Simons Foundation Flatiron Institute in New York City. The successful
-
: 262971 Minimum Education and Experience: Applicants must have a doctoral degree in statistics, computer science, or a related field. City: Piscataway State: NJ Location: Busch (RU-New Brunswick) Create a
-
industry. The Fellowship Program provides robust learning experiences related to Medical Affairs, Commercial/Marketing, Regulatory Affairs, Clinical Development, etc. The Fellow will spend the majority
-
mammalian or microbial systems, preferred. Familiarity with computational analysis or coding (e.g., R, Python), preferred. Equipment Utilized Physical Demands and Work Environment PHYSICAL DEMANDS: Standing
-
biology and behavior; proficiency in lab- or field- or computational research; strong analytical skills; excellent communication and scientific writing skills; ability to work in interdisciplinary teams
-
-based treatments in the community. The training program is designed to impart the skills necessary for submitting successful career development awards. The emphasis on translational clinical research will
-
discipline expertise and computational areas, such as data science, artificial intelligence (AI), machine learning (ML), generative AI and other technology innovations. Key details of this position include
-
communities in health and disease. The successful candidate will work at the interface of bioinformatics, microbiome ecology, and metabolomics, contributing to both computational analyses and laboratory