How can we better predict which drug is best for which patient? We aim to develop a novel methodological and computational framework that integrates cell line drug screening data, patient genomics and response data and external information to better predict the effect of (experimental) cancer drugs.

Your job will be to develop a learner that makes optimal use of all genomic data sources. The project is embedded within the ZonMW-TOP ‘Compute Cancer’ collaborative project.

We are looking for a Postdoctoral researcher with the following background:

• You hold a PhD in Statistics, Mathematics, Bioinformatics, Machine Learning or a closely related discipline;
• You have a strong background in developing new computational and/or statistical methods;
• You have good programming skills, preferably in R;
• You are able to explain complex ideas to biologists/non-specialists;
• Knowledge of any of the areas involved in the project (in particular supervised learning) are an advantage, and so is experience with processing genomics data;
• You have good communication skills, you are self-disciplined and can work in a multi-disciplinary environment.

Salary Scale 10: 2826 tot 4481 euro gross when employed full-time (depending on qualifications and experience).

Salary scale 10: 2826 tot 4481 euro based on a 36 hour week, (depending on qualifications and experience).

We offer you the opportunity for development, deepening and broadening, additional training and a place to grow! Working at AUMC means working in an inspiring and professional environment where development is encouraged in every respect.

In addition we offer:

• Additional benefits include an 8.3% end-of-year bonus and 8% holiday pay;
• Your pension premium will be paid by us for 2/3;
• We reimburse 75% of your OV travel costs (commuting costs).

For more information about our employment conditions, please visit our website. For Dutch citizens it is mandatory to provide a VOG (a declaration of good behaviour). Do not hesitate to call us!

VUmc

The group of Mark van de Wiel (Biostatistics, AUmc) develops statistical methods for –omics analysis, including ‘weighted learners’. Here, the challenge is to determine weights from co-data (auxiliary information on the genomic features) in an automatic and objective way. The group of Lodewyk Wessels (NKI and TUDelft) works on computational methods for oncology including the development of transfer learning approaches that exploit large cell line response data sets to predict therapy response in tumors. We aim to integrate these two approaches.

The successful candidate will be employed at the AUmc (location VUmc) and will spend two days per week at the NKI. Regular meetings between project partners and oncologists will be held to generate sufficient cohesion and momentum.