Platform Scientist: Comparative Protein Evolution

Arcadia is studying biology with evolution as our guide.

Our tools, methods, and frameworks probe the tree of life to identify innovations, novelties, and organisms that accelerate our basic and translational research.

We're seeking a scientist with deep experience in studying the evolution of natural protein variation using comparative approaches to substantially expand our platform.

The Platform Scientist will develop computational approaches to study how natural protein variation arises and is shaped over macroevolutionary timescales, integrating sequence, structure, and function to ask how, and why, proteins differ across the tree of life.

These contributions will be instrumental in developing new computational tools, validating existing tools, testing novel hypotheses, identifying knowledge and dataset gaps, and driving the creation of new computational approaches for studying protein evolution at Arcadia.

We're particularly interested in candidates whose background sits at the intersection of molecular evolution and biochemistry/biophysics/structural biology.

Top candidates are inventive computational evolutionary biologists with deep experience addressing evolutionary questions related to protein characteristics to unlock novel insights.

They're excited to explore new and challenging datasets, familiar with developing novel analysis pipelines, and thrive in collaborative, open research environments.

Enthusiasm and participation in open science are also required as we routinely share our findings via our open-source pubs.

• Produce and analyze computational workflows to interrogate and integrate protein information across non-model clades.
• Apply phylogenetic comparative methods, ancestral sequence reconstruction, or selection inference to large phylogenomic protein datasets across non-model lineages.
• Use comparative analyses of natural protein variation to inform Arcadia's organism and target selection capabilities.
• Troubleshoot computational challenges independently and creatively.
• Collaborate with cross-disciplinary teams to validate computational tools, contribute to shared research goals, and accelerate broader scientific efforts.