The Munsky Group for Quantitative and Predictive Biology is looking for a Postdoctoral Scientist in Computational Biology.

Our highly collaborative team of computational and experimental scientists is looking for a Theoretical/Computational Biology Postdoc to help further our goals to create predictive quantitative models to understand and control gene expression. In this position, you will gain experience and rapid access to cutting-edge experiments including automated super-resolution microscopy, microfluidics, and sub-cellular optogenetics capable to observe and perturb individual molecules within living cells. You will employ a unique combination of knowledge, technical approaches, and leadership skills to build, analyze, and validate advanced computational models to simulate and predict biological behaviors. Your methods will be used in real time not only to understand biological data, but also to drive future experimental investigations. Come join us and help to revolutionize the observation, analysis, prediction, and control of gene regulation at single-molecule resolution.

Example projects include, but are not limited to:

  • Build stochastic models to reproduce spatially-resolved measurements of protein-gene association, single-gene transcription, and single-mRNA translation,
  • Formulate rigorous, statistically-sound inference methods to identify mechanistic models from noisy experimental data,
  • Employ machine learning and statistics to design improved optical microscopy experiments.

Required Job Qualifications include:

  • PhD in engineering, physics, computer science, mathematics, statistics, or a related discipline.
  • Previous experience and record of peer-reviewed publication in development of stochastic models to explain fluctuations in chemical, biological, or physical processes.
  • Extensive and proven experience using linear algebra and differential equation solvers in Python, Julia, C++, MATLAB, R, Java, or Fortran.
  • Previous experience and record of peer-reviewed publication in machine learning, system identification, control or dynamical systems, or model/parameter inference.
  • Experience in model-reduction approaches to improve efficiency of the computational analysis of discrete stochastic processes or high dimension PDEs.
  • Strong oral and written communication skills.

Preferred Job Qualifications:     

  • Experience teaching undergraduate students on the topic of stochastic processes or inference in quantitative biology.
  • Experience with image processing, especially experience analyzing single-cell fluorescence microscopy videos or single-molecule Fluorescence in situ Hybridization images.
  • Experience with bioinformatics, especially experience analyzing transcriptomics data for the interpretation of single-cell sequencing experiments.
  • Personal and professional commitment to diversity and inclusion as demonstrated by involvement in teaching, research, creative activity, service to the profession and/or diversity/inclusion activities.

More about CSU:

The Department of Chemical and Biological Engineering has an international reputation for excellence in research across a broad array of disciplines, including systems and synthetic biology, advanced materials, and bioanalytical devices including biosensors. Our research efforts are supported by state-of-the-art core and computing facilities on the CSU campus, and our collaborative faculty are engaged in myriad research projects across campus and around the world. The beautiful town of Fort Collins, Colorado offers a rich diversity of restaurants and breweries, kind neighbors, effective public transportation, fantastic hiking and bicycling trails, and is a short distance from exciting recreation opportunities in the Rocky Mountains.

How to apply:

Interested applicants should prepare and submit:

  • a resume/CV documenting previous experience and skills,
  • cover letter describing professional goals and interest in the position,
  • one (1) or more first or co-first author publications or electronic pre-prints describing previous work, and
  • contact information for three (3) professional references (references will not be contacted without prior notification to candidates).

All materials should be submitted electronically via:

This posting will remain open until filled, however, for full consideration, applications must be received by 11:59 PM (MST) on November 12, 2023.