The successful applicant will hold a doctoral degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical work.
Previous experience in developing computational methods and implementing them in software in a scientific context is expected. Expertise in analysis and integration of multiomics data, statistical interpretation and analysis of next-generation sequencing datasets is very beneficial, as is communicating results in scientific conferences and papers.
We especially seek candidates with prior experience in statistical aspects of genomics, including gene expression data analysis, GWAS and analysis of NGS data. A good foundation in, and previous usage of methods in any of the following fields is advantageous: statistics, machine learning, optimization and dynamical systems. A background in biology, or previous experience tackling biological questions is beneficial but not necessary.
Proficiency with a high-level programming language (e.g., C++, Java) and/or appropriate scripting languages, and statistical data analysis tools such as R, MATLAB or Python is required.
The ideal applicant should have demonstrated the ability to work independently and creatively. (S)he should have excellent communications skills and be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting, communicating with wet-lab and computational partners.