In collaboration with the Amsterdam Data Science Center, we will combine advanced epidemiological and statistical methodology with novel artificial intelligence solutions. This will lead to new methodology to solve a widely recognized problem in epidemiology and data science; and provides the data scientist with a network and methods from outside the data science field. More attention to the reliability of data and causal relationships within datasets will be crucial for future models predicting all sorts of medical conditions in and outside clinical settings, and both for medical statistics and for artificial intelligence.
Our datasets consist of the cohorts hosted by the Amsterdam Public Health and Amsterdam Cardiovascular Sciences research institutes of the Amsterdam UMC. The data of these cohorts are directly linked to real patients and citizens, as well as various registries, with high relevance for health care, prevention and medical development. A list of these cohorts may be found here: https://www.amsterdamumc.org/en/research/institutes/amsterdam-public-health/strengths/aph-cohorts.htm
Accurate measurement is key in all research. Measurement of the characteristics of study participants is important for diagnostic and prognostic research, and to predict the occurrence of diseases in the near and distant future. Accurate measurement is also important for the definition of outcomes and variables in epidemiological studies, and to reliably establish causal relationships.
Among the many machine learning methods proposed to discover strong but unexpected heterogeneous treatment effects, causal forests stand out for their provably reliable confidence intervals. In the first subproject, we will investigate whether they are suitable for detecting treatment effect heterogeneity in cohort data. Because the APH/ACS cohort data is rich in covariates (features), a random forest based solution is very appealing. Particular attention will be paid to their sensitivity to unmeasured confounding and ability to incorporate longitudinal data.
Selection bias and covariate shift
If the data to train a model on are a skewed representation of the target population or target data, then the model may suffer from selection bias. For example, when the admission to the cohort or dataset depends on the availability of model predictors. A similar problem arises when participants become missing over the course of the follow-up period, for example due to mortality or study-fatigue. When the training-set and the test-set for developing a model originate from the same dataset, covariate shift may occur, which may lead to a biased model as well. This problem is extremely relevant for longitudinal cohorts, especially when multiple measurements are being used. In one of the envisioned projects, the new staff member will investigate selection bias in several Amsterdam UMC cohorts and develop AI-driven solutions for it.
Record linkage as a solution
Besides solutions that may be intrinsically linked to the data-science methodology, a solution may also be sought outside the dataset at hand, for example by linking with other data. In our cohort studies, genetic data may be linked to medical data; or data from cancer registries or Statistics Netherlands. In the third sub-project, the researcher will collaborate with our ‘Krachtige Koppelingen’ project and compare different solutions – both from traditional statistics and from AI – to link different types of data to come to causal inferences.
The results of these projects will be used as starting point for subsequent funding requests.
You will working at the Amsterdam UMC, location AMC. You will be affiliated with the Data Science Center of the UvA, which provides the necessary knowledge in artificial intelligence and computing experience that goes beyond the department’s current expertise. By working one day a week at the Amsterdam DSC and participating in regular team meetings between UvA and Amsterdam UMC, we will learn from each other and be able to design follow-up projects in Data Science.
You will be the key person linking the many cohorts of the Amsterdam UMC together or to other cohorts and public health data. You research will solve the long-standing problem of selection bias and attrition bias in health research with long follow-up times. And you will further develop connections and collaborations between the Amsterdam UMC and the Amsterdam DSC.
As this is a relatively new role, we are looking for data scientists with good ideas about how collaboration between different groups can be ascertained and promoted, and who can work independently in a collaborative spirit.
You are a data scientist optima forma, meaning that you use appropriate statistical techniques and predictive analytics on available data to deliver insights and discover new relations. More specifically, we are looking for a:
We offer you ample opportunity for development, deepening and broadening, additional training and a place to grow! Working at AMR means working in an inspiring and professional environment where development, in all aspects, is stimulated.
For an overview of all our other terms of employment, please visit www.werkenbijamc.nl/arbeidsvoorwaarden-amr.
The department of Epidemiology and Data Science of the Amsterdam UMC, location AMC (i.e. the Faculty of Medicine of the University of Amsterdam), will be the main host department for this position. It is renowned for its methodological research, big statistics research and longitudinal cohorts.
The project will be embedded in the methodology program of the Amsterdam Public Health research Institute, which brings together researchers from AMC, UvA, VUmc and VU. The methodology program includes a wide variety of expertise, ranging from medical doctors interested in research methodology and epidemiologists, to biostatisticians, informatics experts and econometricians. The project will also be supported by the Amsterdam Cardiovascular Sciences research institute, providing the clinical background necessary for the project.
The new staff member will be affiliated with the Data Science Center of the UvA, which provides the necessary knowledge in artificial intelligence and computing experience that goes beyond the department’s current expertise. By working one day a week at the Amsterdam DSC and participating in regular team meetings between UvA and Amsterdam UMC, we will learn from each other and be able to design follow-up projects in Data Science.
During the publication period, applications will be handled continuously. If the vacancy is filled, it will be closed prematurely.
Do you have any questions? For substantive information, please contact Dr. Mariska Leeflang, Associate professor Epidemiology and Data Science via email@example.com.
For more information about the application procedure, please contact Tanja Hart, Recruitment Advisor, via firstname.lastname@example.org or via 06-21603178.
Given the collaboration with HAN University of Applied Sciences for this vacancy, your application may be shared externally with a person who is part of the selection committee in this application process.
A reference check, screening and hiring test may be part of the procedure. Find out here if this also applies to you. If you join us, we will ask for a VOG (Certificate of Good Conduct).
Internal candidates will be given priority over external candidates in the event of equal suitability.
Acquisition in response to this vacancy is not appreciated.