Postdoctoral Associate
About The Position
The research group of Prof. Ramon Grima invites applications for two Postdoctoral Research Associates to join a program focused on the mathematical and computational theory of stochastic gene expression. The group recently relocated to Stony Brook University, and the appointments offer the opportunity to help establish the lab’s research program at its new home while contributing to an active, internationally collaborative research agenda. The successful candidates will work at the interface of applied mathematics, statistical inference, machine learning, and quantitative and systems biology. They will develop methods connecting single-cell and spatial transcriptomic data to mechanistic, biophysically grounded models of gene regulation. Work may also include exact and approximate solutions of stochastic models of gene regulatory systems with complex dynamics, including steady-state and time-dependent mRNA and protein number distributions, first-passage time distributions to threshold crossing, and mutual information rates. Applications span spatial and non-spatial systems, discrete and continuum Markovian frameworks such as chemical master equations and Fokker–Planck equations, and non-Markovian approaches. The Grima group develops mathematical and computational theory of stochastic gene expression, combining exact and approximate stochastic model solutions with noise-decomposition and inference methods to extract kinetic and regulatory information from single-cell data. Its work also uses computational tools for stochastic simulation, machine learning, and deep learning, in collaboration with mathematicians, physicists, and experimental biology groups internationally. The appointment is with the Research Foundation of SUNY. Employment is subject to Research Foundation policies and procedures, sponsor guidelines, and funding availability. The position is FLSA exempt and not eligible for overtime provisions; the minimum salary threshold must be met to maintain this exemption. Applicants should include a resume/CV and cover letter with the online application.
Requirements
- PhD or foreign equivalent in Applied Mathematics, Physics, Theoretical/Computational Biology, Bioengineering, or a closely related quantitative field, in hand by the start of the appointment.
- Strong background in one or more of stochastic processes, probability theory, dynamical systems, statistical mechanics, machine learning, or applied/computational mathematics.
- Demonstrated ability to carry out original mathematical derivations and/or develop computational tools, evidenced by publications, preprints, or thesis work.
- Proficiency in a scientific computing language such as Python, MATLAB, Mathematica, Julia, R, or C/C++.
Nice To Haves
- Prior experience with the Chemical Master Equation, stochastic simulation algorithms, and approximation methods.
- Experience with single-cell or spatial transcriptomic data analysis.
- Familiarity with machine learning and deep learning methods, such as CNNs, LSTMs, and transformer architectures, applied to biological data.
- A track record of independent or co-led research projects and first-author publications.
Responsibilities
- Develop mathematical and computational approaches, including machine learning tools, to study gene activation, deactivation, and interactions, and fit models to single-cell data to assess how well they explain observed data.
- Co-author manuscripts for submission to leading quantitative biology, applied mathematics, and biophysics journals.
- Present research at group meetings, seminars, and international conferences.
- Mentor graduate and undergraduate students as opportunities arise.
- Perform other duties as assigned.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
