Scientist I: Regulatory Genomics for Brain Cells (Hybrid)

Allen Institute; Washington (District of Columbia); Híbrido; USD 87 000 - 108 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Relocation assistance

Visa sponsorship

Benefits package

Resumo da oferta

Allen Institute in Washington state seeks a Scientist I to advance regulatory genomics, turning cross‑species single‑cell multiomic atlases into training and evaluation data for brain health research.

The role collaborates with machine learning colleagues to set design objectives for lab‑in‑the‑loop cycles, interpret enhancer screens, and publish findings while developing a scalable platform for synthetic regulatory elements in any tissue or cell type.

Qualificações

• Proficiency in Python or R for large, high‑dimensional biological datasets.
• Experience with single‑nucleus multiomic, spatial, methylation, or 3D genome data, and enhancer screens such as MPRA, STARR‑seq, or enhancer‑AAV assays.
• Research in gene regulation or regulatory genomics with publications.
• Working familiarity with sequence‑based deep learning and AI‑assisted research tools.

Responsabilidades

• Curate training, validation, and benchmark sets from cross‑species single‑nucleus multiomic data.
• Define cell‑type targets and design objectives for synthetic enhancers from whole‑brain single‑cell atlas data.
• Apply sequence‑to‑function models and guided optimization to prioritize enhancers, and interpret them via motif content, syntax, and spacing.
• Plan design‑and‑test cycles with experimental colleagues, set pre‑defined success criteria, and feed screening results back into model training.
• Publish findings and openly release validated sequences, benchmarks, and documentation.

Conhecimentos

Python

R

Genomics

Neuroscience

Formação académica

Ph.D. in genomics, computational biology, neuroscience, genetics, or related field

Descrição da oferta de emprego

Allen Institute in Washington state seeks a Scientist I to advance regulatory genomics, turning cross‑species single‑cell multiomic atlases into training and evaluation data for brain health research.

The role collaborates with machine learning colleagues to set design objectives for lab‑in‑the‑loop cycles, interpret enhancer screens, and publish findings while developing a scalable platform for synthetic regulatory elements in any tissue or cell type.