Sort by
Refine Your Search
-
0001 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job Overview Working with the U-Spatial team, this position will support geospatial research and teaching across
-
sampling from embayments within the St. Louis Estuary and aims to create a spatial and temporal map of cyanobacterial occurrence in estuary waters. Job Tasks: Sediment sample processing (freeze-drying
-
. This position will work under the direction of Bruce Hammer, PhD. Responsibilities: • Work with Bionanotechnology personnel at the Minnesota Supercomputing Institute on Spatial transcriptomics of embryoid bodies
-
postdocs, staff, and students) in the above Research administration and compliance (15%) • Assist in data storage, entry, and verification for ongoing research efforts using paper and electronic records
-
combination of related education and work experience equal to 4 years Experience and/or coursework using GIS and spatial data Experience and/or coursework in both R and Python programming Experience with one
-
: Support faculty (and potentially postdocs and doctoral students) in the development of book manuscripts and translation of research for lay audiences Establish a pathway for students to publish op-eds Lead
-
% Cytokine and Genomics Analysis: Conduct and assist in the interpretation of cytokine and spatial genomics data. Collaborate with external cores and partners on sample preparation and downstream analysis
-
instrumentation and offering an array of services, including next-generation sequencing, long-read sequencing, expression analysis, genotyping, epigenomics, single-cell and spatial genomics, metagenomics, as
-
dynamics of T cell clonality within the hair follicle. The spatial landscape of immune cells, hair follicle stem cells, stromal cells, nerve cells, etc. will be determined using spatial transcriptomics
-
Qualifications: • Ph.D. in biodiversity informatics, remote sensing of biodiversity, or related fields, and a strong background in implementing advanced machine learning and deep learning approaches, spatial