-
://stanfordsciencefellows.stanford.edu/apply(link is external) Stanford Energy Postdoctoral Fellowship: https://energypostdoc.stanford.edu/(link is external) Data Science Fellowship: https://datascience.stanford.edu/programs/data-science
-
renewable Appointment Start Date: As soon as possible, but later starting date also considered Group or Departmental Website: https://www.mignotlab.com(link is external) How to Submit Application Materials
-
: https://amboies.com/(link is external) How to Submit Application Materials: Applications will be reviewed beginning September 1, 2026 and will continue until the position is filled. Please submit
-
: 2 years Appointment Start Date: 09/2026 Group or Departmental Website: https://med.stanford.edu/huanglab(link is external) How to Submit Application Materials: Interested applicants should send
-
of scientific principles. Strong molecular and cell biology techniques, as well as experience working with mouse and human models. Strong general computer skills, experience with databases and scientific
-
, immunoprecipitations, cloning), FACs/sorting, colony assays, lentiviral and retroviral transductions, transplantation experiments, xenograft models, peptide stimulation assays, single cell sequencing, and RNA-seq
-
, or interoception. Adipose tissue biology, thermogenesis, metabolic physiology, glucose and lipid metabolism, obesity and diabetes models, or mouse metabolic phenotyping. Single-cell genomics, spatial transcriptomics
-
research project utilizing innovative techniques including cell culture and organoid systems, single-cell analyses, and disease models. Responsibilities will include: leading independent research projects
-
biology, cancer biology, molecular biology, immunology, and bioinformatics are all welcome to apply. Lab techniques include mammalian cell culture, mouse models, standard molecular biology and biochemistry
-
complex endometrial models and optimizing in vitro implantation assays. Culturing human embryos and generating stem cell-based embryo models. Tissue sectioning for advanced spatial transcriptomic analysis