Quantitative Biointerfaces for Cell Biology
Research group seeks scientists to study how cell behavior is affected by its environment, using techniques from multiple fields like biology and data analysis.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: May 1, 2025 (Naval Research Laboratory opportunity)
Funding Amounts: $99,200 stipend plus $3,000 travel allowance; relocation and health insurance benefits available.
Summary: Postdoctoral fellowship at Naval Research Laboratory to study how extracellular biointerfaces influence cell biology using interdisciplinary approaches including molecular assays, surface chemistry, microscopy, nanofabrication, and machine learning.
Key Information: Open to U.S. citizens and permanent residents; applicants must have a Ph.D. and be within 5 years of degree; must contact Research Adviser prior to applying.
Description
This postdoctoral research opportunity at the Naval Research Laboratory (NRL) focuses on the interdisciplinary study of cellular processes such as adhesion and migration by precisely controlling and analyzing biointerfaces. The research merges materials science, biophysics, cell and molecular biology to understand how the biophysical and biochemical properties of the extracellular environment regulate cell phenotype and state.
The program seeks talented scientists with expertise spanning molecular/cellular in-vitro assays, surface chemistry, live cell microscopy and quantitative image analysis, micro/nanofabrication, and machine learning/data analytics. The goal is to elucidate how extracellular biointerfaces influence cell behavior through quantitative and innovative experimental and computational methods.
Key research areas include:
- Molecular and cellular biology assays
- Surface chemistry and nanoplasmonics
- Live cell imaging and quantitative image analysis
- Micro- and nanofabrication techniques
- Machine learning and data analytics applied to biological imaging
References from the research group highlight advances in machine learning for live cell image segmentation, surface activity of biofunctionalized materials, and nanoplasmonic detection technologies.
