Computational Biologist, Tree of Life

Cultivarium•London, England

51d•Onsite

About The Position

Cultivarium builds scientific tools that turn biological discovery into real-world capability. As a Frontier Research Contractor, we provide technical solutions and hands-on support to partners working on the problems of today. Born a Focused Research Organization, we are drawn to the hardest technological problems in biology and built to tackle them with intensity. We prize builders who efficiently leverage resources to deliver tangible solutions at the frontier, for outsized societal benefit. Our ambition is to study and engineer non-model organisms across the entire tree of life. We're looking for a computational biologist who is genuinely curious about the strange corners of biology, and rigorous about the genomics that explain them. You'll apply population and comparative genomics to organisms across the tree of life, building the pipelines and analyses that turn raw sequence into biological insight. A successful candidate is fluent in population genomics and organellar genetics, comfortable working at scale on messy datasets with no off-the-shelf reference workflow, and drawn to exotic systems most labs never touch. You'll create impact by bringing deep genomic expertise to non-model systems, developing novel approaches to analyze and integrate diverse biological datasets, and turning population-scale sequence into evidence that drives decisions. This is an in-person role at either our Watertown, MA USA or London, UK location.

Requirements

You believe there's tremendous signal hiding in noisy biological measurements, and that we've barely begun to tap what's possible with the data we already have and the experiments still to come.
Authorized to work without sponsorship.
PhD in population genetics, evolutionary biology, computational biology, genomics, or in a related field.
Demonstrated experience with population- and comparative-genomics methods at population scale, including variant calling, annotation, and comparative analysis using tools such as GATK, bcftools, vcftools, PLINK, ANGSD, and scikit-allel, orchestrated in pipelines (e.g. Nextflow, Snakemake).
Proficiency in Python and/or R with standard bioinformatics libraries (e.g. Biopython, Bioconductor, pandas), and collaborative version control (Git).
A strong scientific communicator who presents complex analyses clearly to interdisciplinary audiences, with a creative, detail-oriented, collaborative approach.

Nice To Haves

Hands-on experience analyzing large, population-scale genomic cohorts.
Experience with organellar genome analysis (e.g. mitochondrial and chloroplast variation, heteroplasmy, and cytonuclear interactions).
Experience with long-read sequencing, structural variation, or pan-genome analysis.
Familiarity with machine learning or foundation models applied to biological sequences (e.g. ESM, Evo, Nucleotide Transformer).
Experience with cloud computing environments (AWS, Google Cloud, or Azure) for large-scale data processing.
A track record of scientific contributions through publications, open-source software, or equivalent demonstrated expertise.
A genuine fascination with exotic non-model systems. Bonus for hands-on experience with any of: - Oxytricha (programmed genome rearrangement) - sacoglossan sea slugs (kleptoplasty, stolen-plastid retention) - apple snails, Pomacea (camera-eye regeneration) - mitochondria and chloroplasts (cytonuclear coevolution) - tardigrades (anhydrobiosis and extremotolerance) - bdelloid rotifers (ameiotic evolution and pervasive horizontal gene transfer)

Responsibilities

Design and run pipelines for population- and comparative-genomic analysis across non-model organisms (e.g. variant calling, population structure, selection scans, and phylogenomics) and extract robust signal from population-scale datasets.
Assemble, annotate, and curate genomes and pan-genomes for organisms with no existing reference, and integrate genomic, transcriptomic, and phenotypic data to connect genotype to phenotype.
Analyze nuclear and organelle, such as mitochondrial and chloroplast, genome variation including heteroplasmy, cytonuclear coevolution, and haplotype structure within and across populations and species.
Apply machine learning where it adds real leverage (e.g. predictive modeling of gene function and trait architecture).
Partner with wet-lab scientists to design experiments and interpret results, and generate clean, well-documented, reproducible code.
Present your work, mentor teammates, and share computational-biology best practices.
Bring high agency and high standards: take ownership, give and receive candid feedback, and push the team to be its best in service of the science.