Platform 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 Platform team builds evolution-aware statistical and ML methods that use phylogeny as structure, making inference more reliable and more generalizable. The result is shared infrastructure that supports research across Arcadia. Read more about our work through our publications.   We are seeking a Platform Fellow to join our Platform team for a 3-month fellowship. Platform uses phylogeny to guide the development of many of our statistical models, machine learning tools, and evolutionary frameworks, powering research across Arcadia. 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, or for researchers looking to apply their quantitative skills to evolutionary biology in new ways.   Platform Fellows will work closely with our computational teams on projects at the intersection of machine learning, statistics, and evolutionary biology. 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:   - Probabilistic modeling and statistical inference - Supervised and unsupervised learning for high-dimensional biological data - Interpretability methods development for ML models - Phylogenetic inference and evolutionary modeling - Comparative and evolutionary genomics across species - Quantitative and population genetics, including human genetics - Analysis of natural selection, adaptation, and trait evolution - Statistical and machine learning approaches to quantitative genetics