Postdoctoral Fellow — Computational RNA Biology

RNA therapeutics are reshaping modern medicine—from mRNA vaccines to precision gene modulation. At the COBRA Lab, we advance computational methods that connect RNA structure to function and translate those insights into tangible health benefits. Joining our team means collaborating with researchers across disciplines, seeing your algorithms inform real-world therapies, and contributing to an inclusive scientific community committed to open, rigorous inquiry.

Appointment

• Term: One year with possibility of renewal
• Salary: CAD $70,000 per year, plus full University of Alberta benefits
• Start date: Flexible (applications reviewed on a rolling basis)
• Work mode: On site in Edmonton

Research Focus

You will collaborate on projects that:

• Characterise RNA structure–function relationships
• Improve RNA efficiency through informed RNA design
• Predict the effects of genetic mutations on RNA behaviour

All projects share the goal of accelerating RNA-based therapeutic development.

Primary Tasks

• Develop and refine algorithms for RNA secondary and tertiary structure prediction, analysis, and design
• Integrate experimental data with computational models
• Co-author manuscripts and present findings at scientific meetings with lab colleagues
• Contribute to research grant writing
• Mentor graduate and undergraduate researchers and promote an open, inclusive lab culture

Essential Qualifications

• PhD (completed or near completion) in Computer Science, Computer Engineering, Bioinformatics, Computational Biology, or a related discipline
• Demonstrated expertise in RNA structure prediction (e.g., dynamic programming, graph grammars, molecular modelling)
• First-author publication in a recognised bioinformatics or computational biology venue (ISMB, RECOMB, WABI, ACM BCB, Bioinformatics, Nucleic Acids Research, etc.)
• Proficiency in Python and C/C++, plus experience with high-performance or cloud computing resources

Desirable Experience

• Machine-learning approaches for molecular problems
• Statistical inference, probabilistic graphical models, or thermodynamic parameter optimisation
• Collaboration with experimental teams or analysis of high-throughput structural probing datasets