PhD position on AI methodology for prediction of patient outcomes using organoid models

Patient-derived organoids are the hallmark of personalized medicine with seemingly endless possibilities to match the right treatment to the right patient. As organoid data are often longitudinal in nature (e.g. expressed by growth curves), can be derived from various markers, and can be obtained under various experimental conditions, these data form a huge analytical challenge. Furthermore, individual predictions of relevant patient outcomes based on such complex biological data remains difficult, especially in the context of rare diseases such as cystic fibrosis. So far, there is a lack of fundamental methods research to guide in these analytical challenges.

In this PhD-project, you will propose and evaluate new AI methodology to ensure that organoid data can be used to optimally predict relevant patient outcomes. You will use a real-world case study on cystic fibrosis that allows the new methods to have direct impact.

You will be part of a multidisciplinary team of statisticians, data scientists, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis.

The supervisory team will consist of dr. Maarten van Smeden, prof. dr. Jeffrey Beekman and dr. Danya Muilwijk.

The maximum salary for this position (36 - 36 hours) is € 3.108,00 gross per month based on full-time employment.

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).

The Data Science team at the Julius Center is a growing group of researchers working on methods and applications of AI in health care. The PhD candidate will be embedded in the AI methods lab of the UMC Utrecht. Furthermore, you will work in close collaboration with laboratory experts of the cellular disease models lab and clinicians of the dept. of pediatric pulmonology of the UMC Utrecht.