Sort by
Refine Your Search
-
cytometry, cell culture, immunohistochemistry (IHC), and animal models. Analyze and interpret complex immunological and molecular data derived from preclinical studies. Collaborate with academic and clinical
-
to those who have expertise in neuroanatomy, small animal surgery, electrophysiology and behavioral pharmacology. Prior working experience with rodent models. Small animal stereotaxic surgery experience
-
animal models and extending these insights to understanding and intervening in human diseases. Successful candidates will be self-motivated scientists, with a passion for advancing cutting-edge research
-
Classification Title: Post-Doctoral Associate Classification Minimum Requirements: Must have PhD in astronomy or physics. This position will be initially awarded for one year, and, contingent upon
-
studies. Develop and apply advanced statistical methods and machine learning techniques using tools such as R and Python. Integrate and run process-based models (e.g., crop models, hydrologic models
-
process. When ready, the hiring department will contact the listed references via email, requesting their reference letters to be uploaded directly to the application website. The final candidate will be
-
. The postdoctoral researcher will also manage day-to-day laboratory operations and apply moderately complex plant molecular biology techniques to transform and analyze citrus genomes, process biological samples, and
-
through the process by leading application workshops, coordinating campus interviews, and offering strategic advising. Fellowship Programming and Outreach Designs and delivers programming on prestigious
-
) mathematics, physics, or a related STEM fields. Job Description: The Division of Biomedical Informatics and Data Science (BIDS) within the Department of Health Outcomes and Biomedical Informatics (HOBI
-
science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields. Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep