The presence of missing data is an important problem when conducting empirical research and implementing research findings in clinical practice. Although several statistical methods for dealing with missing values are available (e.g. imputation), they have limited usefulness when generalizability is required across different settings and populations. This situation typically arises when combining (incomplete) patient-level data from multiple cohort studies, and performing a meta-analysis.
As a PhD student, you will investigate statistical methods for dealing with missing values in pooled cohort studies. This research is part of ReCoDiD (‘Reconciliation of Cohort data in Infectious Diseases'; https://recodid.eu/), a project supported by the European Commission under the Horizon 2020 Programme and by the Canadian Institutes of Health Research Institute of Genetics. The consortium brings together a multidisciplinary team from four continents to fast track the research response to viruses and other pathogens by facilitating data and sample sharing between infectious disease cohort studies.
The aims of this position are to develop, evaluate and implement statistical and machine learning methods for dealing with missing values in large heterogeneous datasets with clustering. To this purpose, the PhD student will investigate advanced imputation methods that can account for measurement error, time-varying exposures and outcomes, and data that are missing not at random. In addition, imputation methods will be developed that can be implemented in sparse datasets and applied to new individuals from different settings and populations.
This job is based on a temporary position for 3 years. The salary for this position is determined by the OIO scale, and will start at step 0. The terms of employment are in accordance with the Cao University Medical Centers (UMC).
The PhD student will work in the epidemiology department of the Julius Center for Health Sciences and primary care, and be part of the "Big data methods" research group. This group focuses on the development and evaluation of statistical methods for analyzing large datasets that are commonly obtained via meta-analysis of individual participant data or from registries with electronic healthcare records. In this project, we will collaborate nationally and internationally with leading experts from multiple disciplines, including Biostatistics, Epidemiology, and Infectious Diseases.
You have an advanced university degree (Master's degree) in statistics or a related field is required. You are interested in the development and application of statistical methods
in epidemiology. You have an advanced understanding (i.e. theory, estimation methods and assumptions) of regression models, mixed effects models and survival models. You also have an advanced understanding of Markov Chain Monte Carlo algorithms (notably Gibbs sampling and bootstrapping methods). You are familiar with machine learning methods (including boosting, bagging, random forests, neural networks, support vector machines).
You are proficient with statistical software packages (especially R, but also WinBUGS, JAGS and/or Stan). Good writing and communication skills in the English language are required.
You have a high level of motivation, the ability to work in a team, a proactive
personality, scientific curiosity and willingness to learn.
The maximum salary for this position (36 - 36 hours) is € 3.196,00 gross per month based on full-time employment (work week 36 hours).
In addition, we offer an annual benefit of 8.3%, holiday allowance, travel expenses and career opportunities. The terms of employment are in accordance with the Cao University Medical Centers (UMC).
If you have any questions about this vacancy, please contact Thomas, Assistant Professor, phone number: 0646 16 15 98, e‑mail adress: T.Debray@umcutrecht.nl.
Acquisition based on this jobopening is not appreciated.