15 часов назад
Computational Biologist, Immune Cell Repolarization (AI)
153 000 - 191 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Computational Biologist, Immune Cell Repolarization (AI): Developing and evaluating machine-learning and multi-omics methodologies to model tumor-immune-stromal crosstalk and identify therapeutic targets, with an accent on transcriptomic, TCR-Seq, and spatial data analysis. Focus on building interpretable probabilistic and deep-learning models, validating computational methods on biological data, and translating findings into publications and licensed technologies.
Location: New York, NY (Hybrid); onsite at least 60% of the working month, approximately three days per week.
Base pay: $153,000–$191,000 per year, with possible eligibility for a discretionary annual performance bonus.
Company
brings frontier AI models, large-scale computing, biological foundation models, and experimental capabilities together to accelerate scientific discovery and develop disease treatments.
What you will do
- Develop, apply, and evaluate computational and AI methodologies for biological research.
- Build mechanistic models of tumor-immune-stromal crosstalk using transcriptional, TCR-Seq, spatial, clinical, pre-clinical, and other relevant datasets.
- Develop, test, and validate models in collaboration with interdisciplinary research teams.
- Communicate research progress and results to internal and external colleagues.
- Publish findings through preprints and software repositories such as GitHub.
- Support patenting and licensing of technologies resulting from the research.
Requirements
- PhD in Systems Biology, AI/Machine Learning, or Statistics, or an MS degree with relevant professional experience.
- 1–2 years of relevant biomedical science experience and strong knowledge of cellular biology, transcription, and protein signal transduction.
- Ability to create, implement, and evaluate computational methodologies using machine learning, statistics, and AI.
- Programming experience in R and Python.
- Experience building and evaluating machine-learning or neural-network models on biological data, including feature selection, regularization, model introspection, and interpretability.
- Experience using or modifying probabilistic-learning or deep-learning models, such as RNNs, GNNs, protein sequence models, or NLP models.
Culture & Benefits
- Collaborative, interdisciplinary research environment connecting Columbia University, The Rockefeller University, and Yale University.
- Focus on scholarly excellence, innovation, open communication, hands-on hacking, partnership, and open science.
- Employer matching on employee 401(k) contributions.
- Paid time off for volunteering and funding for select family-forming benefits.
- Relocation support is available for employees who need assistance moving.
Hiring process
- Submit a cover letter with the resume.
- Work schedule and specific in-office days are communicated during the interview process and determined by the hiring manager.
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