Are you interested to work in the exciting field of genomics (big data) and eager to improve the prognosis of bladder cancer patients? Join our team!

Many diseases recur after recovery, e.g. recurrences in cancer and infections. Research into these recurrences is mainly focused on analyzing time-to-first recurrence using commonly applied survival models instead of analysis of the total recurrence burden. Thus, any subsequent recurrences that may occur after the first recurrence are ignored, and hence a substantial part of the clinical data is discarded. There are several statistical models available that enable modeling of the total recurrence burden, so-called recurrent time-to-event models.

However, these models are computationally demanding and therefore inefficient to apply to high-dimensional data (big data) analysis. This also holds for genome-wide association studies (GWAS), in which millions of DNA variants are analyzed simultaneously. Currently, several solutions for this problem are being developed. In this project, the PhD candidate will evaluate (and optionally: co-develop, extend and/or improve) these recurrent event methods for application in the context of GWAS.

Next, the PhD candidate will apply the recurrent event models to non-muscle invasive bladder cancer (NMIBC). NMIBC is an example of a disease that is characterized by a high risk of recurrences: >50% of the NMIBC patients experiences at least one recurrence within three years after diagnosis, and many patients experience multiple recurrences. This necessitates frequent follow-up visits and repeated surgery with adjuvant treatment, placing a heavy burden on patients' quality of life. The PhD candidate will perform a recurrent event GWAS using data from the Nijmegen Bladder Cancer Study and data of cohorts of international collaborators (these are all existing datasets) in order to identify novel genetic variants that are associated with the NMIBC recurrence rate.

Taken together, this project focuses on evaluation and application of state-of-the art methodology using a wealth of clinical and genetics data as obtained from bladder cancer patients. In addition, this project will allow you to contribute to the development of a personalized surveillance and treatment plan for NMIBC patients.

Tasks and responsibilities

• Comparing and evaluating recurrent event methods for application in the context of GWAS
• Data cleaning and data management of big data, i.e. clinical data and genomics data of NMIBC patients.
• Execution of a large, international meta-analysis of GWAS for the total NMIBC recurrence burden.
• Working in a multidisciplinary team: work together with researchers from diverse professional backgrounds, e.g. in genetics, epidemiology, bioinformatics, biostatistics, pathobiology, but also with urologists and international collaborators from diverse backgrounds.
• Writing of scientific publications resulting in a PhD thesis.
• Presentation of results at internal and external meetings and national and international (in the future maybe digital?) conferences.
• Possibility to follow courses and training throughout the project to obtain or increase relevant knowledge and skills.
• Possibility to be involved in teaching (curriculum Biomedical Sciences and Medicine).

• Master degree in biomedical sciences/epidemiology/health sciences or a related discipline, preferably with experience/strong interest in genetic epidemiology/statistical genetics or a degree in (bio)statistics/data science with a strong interest in medical sciences.
• Experience with quantitative statistical analyses.
• Preferably experience with data cleaning and (big) data management.
• Programming skills (e.g. R, bash) are a pre.
• Eager to learn, pro-active, able to work independently, well-organized, good communication skills and responsible.
• Good verbal and written communication skills in English.

Fixed-term contract: 4 years.

The starting date of this project is negotiable, but at the latest in September 2020.

Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG) and there will be, depending on the type of job, a screening based on the provided cv. Radboud university medical center's HR Department will apply for this certificate on your behalf.

Read more about the Radboudumc employment conditions and what our International Office can do for you when moving to the Netherlands.

Radboudumc

The department for Health Evidence consists of 90 employees with expertise in biostatistics, health technology assessment, systematic reviewing, cancer epidemiology, reproductive epidemiology and risk assessment, offering a multidisciplinary environment for methodological research in medicine. All specializations work in close collaboration with many clinical departments in the Radboudumc. At the department for Health Evidence we aim to improve healthcare and public health by developing, applying and teaching methods for prediction and evaluation research. The department has three main tasks: research, education, and consultation. The focus of all three is on research methodology and data analysis, in the context of Radboudumc's research themes.

Research Institutes
At the moment there are more than 1,300 PhD candidates at our medical hospital. This number includes PhD candidates on our pay roll as well as external candidates (those employed somewhere else but researching on our premises).

• Radboud Institute for Health Sciences: ± 700
• Radboud Institute for Molecular Life Sciences: ± 400
• Donders Center for Medical Neurosciences: ± 200

Read what it is like to do a PhD at the Radboud University Medical Center.

Radboudumc
Radboudumc strives to be a leading developer of sustainable, innovative and affordable healthcare to improve the health and wellbeing of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare. To get a better picture of what this entails, check out our strategy.

Read more about what it means to work at Radboudumc and what our people have to say about it.