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15 часов назад

Computational Biologist, Immune Cell Repolarization (AI)

153 000 - 191 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
junior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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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

hirify.global 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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