Sort by
Refine Your Search
-
Qualifications: Ph.D. in Molecular Biology, Virology, Cell Biology, bioengineering or related field. Strong background in AAV biology, including hands-on experience with AAV vectors in cellular models
-
would include: Co-developing a hybrid machine learning/process-based model of anaerobic digestion processes Performing techno-economic and lifecycle analysis of microgrids build around novel biogas-fueled
-
, and internal equity. Pay Range: $77,000 - $80,000 Postdoctoral Fellow in Large Language Models and Electronic Phenotyping in Cancer We are seeking a highly motivated Postdoctoral Research Fellow with
-
theoretical modeling, remote imaging, and direct observation. Our research has both fundamental and applied elements. The successful candidate will demonstrate exceptional academic achievement and promise, and
-
(community interventions, community-based participatory research, meta-analysis and bias in research, RCT methods, causal interference, mathematical modeling, and econometrics) Policy research related
-
experiences. The Fletcher Lab at Stanford University uses computational systems modeling to advance resilient and equitable water resources management for an uncertain future. Current research topics in the lab
-
, mouse models, standard molecular biology and biochemistry, FACS/flow-cytometry analysis, genetic manipulation of cells (e.g., CRISPR), and microscopy. Candidates with experience in computational analysis
-
, molecular biology, and in vivo models. Analyze and interpret data, integrating experimental and computational findings. Utilize bioinformatics tools and techniques to analyze high-throughput sequencing data
-
diseases, in particular glaucoma. This position involves the application of optic nerve crush and other models of retinal ganglion cell injury to study signaling pathways in vivo. This position will involve
-
Center for Biomedical Informatics Research at Stanford University. This position emphasizes evaluating various cancer screening strategies by developing and applying microsimulation models for decision