Sort by
Refine Your Search
-
contribute to the design, development, testing, and deployment of custom ground-based sensing platforms for efficient and accurate collection of agricultural data. These platforms will support integration
-
an interdisciplinary approach that integrates mitochondrial patch-clamp electrophysiology, advanced live-cell and label-free imaging, structural biology (cryo-electron tomography and cross-linking mass spectrometry
-
on the development and validation of a new humanized computational model of cochlear nerve responses to electrical stimulation under the mentorship of Dr. Ian Bruce. This work will integrate experimental data obtained
-
-population analysis, and integration of genomic data with longitudinal electronic health records. The scholar will have opportunities to lead projects using data from the Global Biobank Meta-Analysis
-
-funded “When Blue is Green ” project. The hires will join an interdisciplinary team aiming to develop integrated zero-waste seafood production systems. Successful candidates will conduct research on one
-
Institute. Our lab focusses on axon-glia interactions and on how glial metabolism regulates axon integrity and myelination (e.g., Nature Neuroscience, PMID 32807950; Nature Neuroscience, PMID 25195104; PNAS
-
—integrating first-principles simulations with machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive
-
professional development, while also supporting teaching and scholarly activities within the Department of Engineering Education. The postdoctoral scholar will divide their effort across three integrated areas
-
translational research approach that integrates human iPSC-based models, in vivo disease models, advanced Ca²⁺ imaging, confocal microscopy, molecular cloning, gene editing, behavioral phenotyping, functional
-
, with an emphasis on Acute Myeloid Leukemia (AML). The successful candidate will apply advanced computational and statistical approaches to integrate and analyze multi-omics and single-cell datasets in