Which biomarkers can predict disease progression in Alzheimer’s disease? Or which factors predict treatment response in acute stroke patients or cancer? Are you going to help us solve these questions?

Recent advances in the analysis of big data allow efficient learning from datasets with many variables on a limited number of individuals. In parallel, there have been important advances in the field of causal inference. Building on these developments, this project aims to tailor regularization methods and causal inference techniques for medium dimensional prediction and treatment effect size estimation problems typical in medical settings, where limited amounts of data are typically collected at high cost. The project entails both methodological development and implementation, and aims to increase accessibility of state of the art methods to a wide audience.

Objectives include methodological developments and implementations that facilitate more effective prediction model development and evaluation in medium dimensional settings (e.g. where there is a clear tension between sample size and the number of model parameters). Challenges include both prognostic and treatment effect modeling. You will have access to high quality clinical data to inspire and implements methods. You will finalize the research with a PhD thesis.

• You hold an MSc in Statistics, Mathematics, Artificial Intelligence, or a closely related discipline;
• You have a strong background in quantitative modeling enabling the development of new computational methods;
• You have strong programming skills, preferably in R, or are willing to learn R;
• You are able to explain complex ideas to epidemiologists/non-specialists;
• Knowledge of regularization techniques, prediction analysis, and causal inference is an advantage;
• You have good communication skills (written/oral) in English, you are self-disciplined and can work in a multi-disciplinary environment.

• An international and multidisciplinary research environment consisting of biostatisticians, epidemiologist, (bio)informaticians and clinicians.
• A contract for one year, to be extended with three years, after a successful evaluation in the first year.
• Your salary will be in salary scale OIO: € 3.017 gross per month in the first year to € 3.824 gross per month in the final (4th) year based on a full-time employment.
• In addition to a good basic salary, you will also receive an 8.3% year-end bonus and an 8% holiday allowance. Calculate your net salary here.
• For more information on our terms of employment see our website.

Amsterdam UMC

The Big Statistics section on the Department of Epidemiology and Data Science has a strong tradition of applying novel mathematical insights into statistics applications for medical purposes. The proposed line of research into medium dimensional prediction also has a strong applied character. Key research lines in our section include: statistical omics, studying statistical methods for big p problems (e.g. gene, protein, metabolite, and exposome data); co-data learning: studying methods that allow incorporating external data or expert knowledge into statistical models; and causal inference. You will be co-supervised by dr. Jeroen Hoogland and prof. Mark van de Wiel from the department of Epidemiology and Data Science.

During the publication period, applications will be handled continuously. If the vacancy is filled, it will be closed prematurely.

If you have any questions about this position, please feel free to contact Jeroen Hoogland, Assistant professor Epidemiology and Data Science, via j.hoogland@amsterdamumc.nl.

For more information about the application procedure, please contact Rhiannon Sandfort, Recruitment advisor, via r.e.sandfort@amsterdamumc.nl.

A reference check, screening and hiring test may be part of the procedure. Read here whether that applies to you. If you join us, we ask you for a VOG (Certificate of Good Conduct).

Internal candidates will be given priority over external candidates in case of equal suitability.

Acquisition in response to this vacancy is not appreciated.