Sort by
Refine Your Search
-
therapeutics. We are seeking a highly motivated, collaborative, and independent Postdoctoral Researcher to spearhead a research program within the general areas of protein biochemistry, engineering, and
-
. Strong background in computational analysis and immunological studies. Experience with data analysis for single-cell or spatial methods is highly desirable. Excellent communication and interpersonal skills
-
are seeking a highly motivated, collaborative, and independent Postdoctoral Researcher to spearhead a research program within the general areas of synthetic genomics and synthetic biology, as
-
using in vivo Perturb-seq. This project is related to a new NIH-funded Program Project Grant aimed at identifying differences and similarities in gene function across vascular cell types and diseases, as
-
genomics, and molecular biology techniques. Applicants with expertise and interest in (1) mass spectrometry-based proteomics, (2) molecular and cell biology including GPCRs, neuroscience, functional genomics
-
will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and
-
backgrounds trained in chemistry, chemical biology, microbiology, and/or biophysics fields. We have launched a collaborative antibacterial drug design program integrating chemical biology and mechanistic
-
Health Epidemiology and Population Health Med: PCOR Health Policy Neuroscience Institute Medicine, Biomedical Informatics Research (BMIR) Biomedical Data Science Postdoc Appointment Term: 1-3 years
-
, single cell and multi-omics data analysis, and a high-performance computing environment (Unix/Linux) is highly preferred. An individual with Next generation sequencing experience is preferred. A good
-
and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning