Postdoctoral Fellow — Stochastic Gene Expression & ML

The Research Foundation for The State University of New York at Stony Brook

Stony Brook (NY)

On-site

USD 52,000 - 75,000

Full time

2 days ago
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Job summary

The Research Foundation for The State University of New York at Stony Brook invites applications for 2 Postdoctoral Research Associate positions in Stony Brook, NY. The roles focus on mathematical and computational theory of stochastic gene expression, with opportunities to collaborate internationally and establish the lab's new home at Stony Brook University.

Candidates will work at the interface of applied mathematics, statistics, machine learning, and quantitative biology, developing methods

Qualifications

  • As evidenced by an attached resume.
  • PhD by start of appointment in a quantitative field.
  • Strong background in stochastic measures and mathematical modeling.
  • Ability to derive mathematical results and develop computational tools.

Responsibilities

  • Develop mathematical and computational approaches, including machine learning tools, to study gene regulation and fit models to single-cell data.
  • Co-author manuscripts for submission to leading quantitative journals.
  • Present research at group meetings, seminars, and international conferences.
  • Mentor graduate and undergraduate students as opportunities arise.
  • Other duties as assigned.

Skills

Stochastic processes
Probability theory
Dynamical systems
Statistical mechanics
Machine learning
Applied mathematics

Education

PhD in Applied Mathematics or related quantitative field

Tools

Python
MATLAB
Mathematica
Julia
R
C/C++

Job description

The Research Foundation for The State University of New York at Stony Brook invites applications for 2 Postdoctoral Research Associate positions in Stony Brook, NY. The roles focus on mathematical and computational theory of stochastic gene expression, with opportunities to collaborate internationally and establish the lab's new home at Stony Brook University.

Candidates will work at the interface of applied mathematics, statistics, machine learning, and quantitative biology, developing methods

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