Postdoctoral positions in Quantitative Biology

Required Qualifications: (as evidenced by an attached resume)

• 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 (Python, MATLAB, Mathematica, Julia, R or C/C++).

  Preferred Qualifications: 

  • 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 (e.g., CNNs, LSTMs, transformer architectures) applied to biological data.
  • A track record of independent or co-led research projects and first-author publications.

    Brief Description: 
    The research group of Prof. Ramon Grima invites applications for 2 Postdoctoral Research Associates to join a program of work on the mathematical and computational theory of stochastic gene expression. The group has recently relocated to Stony Brook University, and this appointment offers the opportunity to help establish the lab’s research program at its new home while continuing an active, internationally collaborative research agenda.

    The successful candidates will work at the interface of applied mathematics, statistical inference, machine learning, and quantitative/systems biology, developing methods that connect single-cell and spatial transcriptomic data to mechanistic, biophysically grounded models of gene regulation. There is also scope to work on exact and approximate solutions of stochastic models of gene regulatory systems with complex dynamics. This includes the derivation of steady-state and time-dependent mRNA/protein number distributions, first-passage time distributions to threshold crossing, and mutual information rates. Applications span both spatial and non-spatial systems, using discrete and continuum Markovian frameworks (chemical master equations and Fokker–Planck equations), as well as non-Markovian approaches.

    The Grima group develops the mathematical and computational theory of stochastic gene expression, combining exact and approximate solutions of stochastic models with noise-decomposition and inference methods to extract kinetic and regulatory information from single-cell data. This work is complemented by computational tools spanning stochastic simulation, machine learning, and deep learning, and is carried out in collaboration with mathematicians, physicists, and experimental biology groups internationally.