Computational Scientist — Origin and Evolution of Life 

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Location: Boston, MA or London, UK.

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At Dayhoff we're reverse engineering the chemistry of life from its origins. Our scientific bet is that the origin of life isn't a curiosity question - it's the missing chapter of biochemistry, the unfilled interpolation between geochemistry and modern enzymology. Writing that chapter gives us foundation models that the rest of the field can't build.

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This role is for someone who wants to do independent research within that programme. Examples we're actively interested in: how did catalysts emerge from prebiotic chemistry, and what does that imply about the structure of the enzyme fitness landscape today? Can we reconstruct the chemical reaction networks that preceded modern biochemistry, and what would it take to design our own? How do thermodynamic constraints shape the emergence of complex chemical systems? What drove the evolution of enzymatic specificity, and is there a quantitative signature of it we can train models against? 

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What we're looking for: a PhD and publication record in computational biology, computational chemistry, systems biology, chemical engineering, or an adjacent field, with real depth in at least one of — chemical reaction network modelling; ML for molecular systems; computational biochemistry (QM/MD, reaction or catalysis modelling); evolutionary modelling (phylogenetics, ancestral reconstruction, population genetics); non-equilibrium thermodynamics or statistical physics. We care more about your ability to drive a research programme to insight and publication-quality research than your specific research experience. You will work with AI Researchers, Computational Chemists and wet lab Scientists. 

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We offer competitive compensation. This role is in-person in Boston or London, with reasonable flexibility. We can discuss relocation and visa sponsorship for the right candidates.

 

To apply, email careers@dayhofflabs.com with:

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1. CV and links to your most important publications or preprints.

2. A page (no more) describing one research project you'd run in your first six months here, what you'd expect to learn, and what would tell you it had failed.