As Postdoc researcher you will employ both reference-based and de-novo assembly approaches in order to quantify and analyze the variability of structural variants in the human genome. We will investigate unique well-phenotyped cohorts of cognitively healthy centenarians (cognitively healthy individuals older than 100 years of age), their family members and a cohort of Alzheimer's disease patients. Our research aim is (i) to study which structural changes affect the risk to develop Alzheimer's disease (ii) to identify structural variations vulnerable for somatic mutations, (iii) to identify and investigate structural variations that modify across generations.

About your role
As Postdoc your main responsibilities are:

• Monitoring the generation of PacBio SMRT long-read sequencing data;
• Development of approaches and algorithms to call structural variations from long-read sequencing data (i.e. variable nucleotide tandem repeats, VNTRs, but also other structural variants);
• Analysis and interpretation of structural variations in the context of Alzheimer's disease and longevity;
• Validation of structural variations using GWAS data (developing imputation approaches) and WGS short-read data. Our lab has a central position in studying WGS/WES data in the Alzheimer's field and this data is available.

• You have a recent PhD degree in Bioinformatics, Genetics or related studies, with a solid computer science background;
• Experience with sequencing data and methods development is required. Experience with PacBio long-read sequencing data is preferred;
• Experience with programming, data visualization and data handling in Python, R, or related languages;
• Proficient in Unix/Linux-based command line and cluster environments; Experience with programming and high-performance computing.
• You are communicative, well organized, independent and willing to supervise.

We are looking for a candidate who has experience with sequencing data and methods development. The research area of interest is rapidly growing, and there is no standard analysis pipeline or method. Programming skills in different languages (python, bash, R) and experience in cluster computing systems is essential. You are highly motivated, creative and willing to supervise. Within this project most communication will be in English, so fluency in English and excellent communication and organizational skills are essential.

• A contract for the duration of the project.
• Salary scale 10: € 3.047 - € 4.800 bruto with a 36 hour week (depending on qualifications and experience).
• In addition to a good salary, we offer, among other things, 8.3% end-of-year bonus and 8% holiday allowance. Curious about what you will earn? Calculate your net salary here.
• Free and unlimited access to the training offer of our online learning environment GoodHabitz
• Your pension is accrued at ABP. Amsterdam UMC contributes 70% of this premium.
• We reimburse 75% of your public transport travel costs and also offer a bicycle arrangement.

Amsterdam UMC

In our lab, based at the Alzheimer Center and the department of Clinical Genetics at the Amsterdam University Medical Center, we are interested in the genetics of escaping cognitive decline. For this we have set up the 100-plus study cohort of cognitively healthy centenarians (www.100plus.nl). It is clear that genetic elements are involved with both the increased of Alzheimer's Disease and with the escape of Alzheimer's Disease.

To learn more about these genetic elements, we will investigate role of large structural genomic variations. For this, we are currently using Pacbio SMRT sequencing to generate long-read sequencing date from hundreds of genomes from well-phenotype patients with Alzheimer's Disease from cognitively healthy centenarians and their family members. By contrasting the genomes from groups who represent the extreme ends of the cognitive spectrum we aim to maximize our effect size of our analysis. Further, we will also investigate the somatic differences between centenarian blood and brain samples. We are looking for a postdoc for three years to analyze these novel, exciting and rich data.