Evolutionary Cell Biology Fellow

A Bit About Us We are Arcadia Science, an evolutionary biology company founded and led by scientists. Our mission is to turn natural innovations into real-world solutions by developing systematic and quantitative approaches to leveraging biology for therapeutics R&D. We share our research as openly as possible to accelerate discovery and make our work broadly useful. The Opportunity We’re closing the gap between biological data and biological understanding. Our Validation team does this by closing the design–build–test–learn loop through lab validation across diverse organisms.  Read more about our work through our publications. We are seeking an Evolutionary Cell Biology Fellow to join our Validation team for a 3-month fellowship. This is an ideal opportunity for scientists in the final stages of their PhD or postdoc training who want to experience industry research in an open science environment working with diverse organisms, or for researchers excited to apply cell biology techniques across the tree of life. Evolutionary Cell Biology Fellows will generate phenotypic and genotypic data to compare cellular features across organisms. Because we use evolution to select the best organism to study each biological question, fellows will work with diverse, often non-traditional organisms and cell types. Fellows will contribute to a defined project with the goal of publishing their work openly by the end of the fellowship. Areas of Focus We are looking for candidates with expertise in one or more of the following areas: - Cell-based or organism-based assay development and optimization - Live cell imaging, high-content screening, or other high-dimensional phenotyping approaches - Diverse cellular organism and system development and/or utilization (including non-model organisms) - Molecular biology techniques (cloning, transformation, PCR, genetic manipulation, protein detection) - Protocol development and adaptation for new organisms or cell types - Comparative analysis between species driven by evolutionary information - Quantitative approaches to phenotypic measurement