Big Data Analysis;
Computational Molecular Biology;
Statistical Signal Processing.
We develop computational methods for inferring RNA dynamics from experiments and theory, with applications ranging from basic research to bimolecular engineering and synthetic biology.
Feb. 2018: New paper on reconstruction of complex RNA structure landscapes in Nature Communications.
March 2018: PATTERNA, a new algorithm that mines RNA structures from large-scale structure profiling datasets, in Genome Biology.
We are seeking highly motivated and energetic people with interest in computational RNA genomics and RNA bioengineering to join our team. We have openings for both postdocs and graduate students.
If you are keen on learning, developing, and applying machine learning methods to analyze RNA structure and dynamics and to engineer novel RNAs, our newly forming team may be a good fit for you.
Projects in the lab range from highly algorithmic/mathematical method development to “moist lab” work that combines simulations, data analysis, and wet lab experiments. Projects are chosen and tailored based on the candidate’s interests and background.
Qualifications: we are assembling an interdisciplinary and collaborative group and welcome creative people from diverse backgrounds. We are especially looking for people with background in one or more of the following areas:
• Signal processing or machine learning
• Chemistry/biochemistry or biophysics
• Biological modeling and statistical analysis
• Genomics/genetics (particularly, next-gen sequencing methods)
• Applied mathematics
To apply: contact Prof. Aviran via email (saviran (at) ucdavis (dot) edu).Specific skills that are desired include proficiency with programming languages, simulations, and scripts (e.g., Python, Matlab, R, C/C++), hands-on genomic data analysis, and/or molecular biology and biochemistry lab techniques.
Graduate students from all UC Davis graduate groups are welcome to inquire with Prof. Aviran about their potential fit to the lab.