We could still use more applicants for this position, so bumping the open position...
Available: Research position Biological Data Visualization and Visual Analytics
Keywords: biological data visualization; visual analytics; data integration; genomics; postdoc
Are you well-versed in the language of Tufte? Do you believe that visualization plays a key role in understanding data? Do you like to work in close collaboration with domain experts using short iterations? And do you want to use your visualization skills to help us understand what makes a cancer a cancer, and what distinguishes a healthy embryo from one that is not?
We're looking for a motivated data visualization specialist to help biological researchers understand variation within the human genome. Methodologies exist for analyzing this type of data, but are still immature and return very different results depending on what assumptions are made. The type of data can also be used for a huge amount of different research questions, which necessitates developing very exploratory tools to support hypothesis generation.
The ideal candidate is well-motivated, holds a PhD (or at least MSc) degree in computer science or bioinformatics, and has experience in data visualization (e.g. using tools like D3 [http://d3js.org] or Processing [http://processing.org]). Prior experience working with DNA sequencing data and genome-wide detection of genetic variation would be an advantage but is not crucial. Good communication skills are important for this role.
You will collaborate closely with biologists and contribute to the reporting of the project. You will be able to work semi-independently under the supervision of a senior investigator, mentor PhD students, and contribute to the acquisition of new funding. A three-year commitment is expected. Start date is as soon as possible.
- Medvedev P, Stanciu M & Brudno M. Computational methods for discovering structural variation with next-generation sequencing. Nat Methods 6(11):S13-S20 (2009)
- Nielsen CB, Cantor M, Dubchak I, Gordon D & Ting W. Visualizing genomes: techniques and challenges. Nat Methods 7:S5-S15 (2010)
- Bartlett C, Cheong S, Hou L, Paquette J, Lum P, Jager G, Battke F, Vehlow C, Heinrich J, Nieselt K, Sakai R, Aerts J & Ray W. An eQTL biological data visualization challenge and approaches from the visualization community. BMC Bioinformatics 13(8):S8 (2012)
For more information and to apply, please contact Jan Aerts (firstname.lastname@example.org, @jandot, +Jan Aerts). If possible, also send screenshots and/or screencasts of previous work.