Postdoctoral fellow in bioinformatics/machine learning - Hiring in process/Finished, not possible to apply

A postdoctoral  postion is available to develop prediction methods for variation interpretation and analysis of large genomic data sets. The major research focus is on developing machine learning algorithms to address types and consequences and mechanisms of variants related to massive genomic data.

Candidates should have a PhD degree in computational biology, bioinformatics or at a related discispline. Strong background in bioinformatics, machine learning, programming, databases and data analysis is required. Knowledge about relevant areas in biology and medicine, molecular biology, structural biology, biochemistry, genetics, etc.) would be beneficial. Strong background in machine learning and statistics required.

Furthermore, the successful candidate should have experience within some of the following areas:

- Experience with data structures, data bases, and data architectures

- Knowledge of biomedical resources and infractructures

- Competences within medical statistics and statistical programming, preferably in R

- Expertise in genomic data analysis

- Expertise working in Unix/Linux environments

- Excellent skills in oral presentation and good writing skills

The position is immediately open for two years.

Salary according to agreement.