This course will cover the whole process of developing and implementing ML-based risk prediction models in the clinic by looking at how to prepare heterogenous longitudinal health data for prediction, how to build dynamic risk prediction models, and how to implement predictive tools in the clinic. The course is intended for PhD students within the field of data science and focuses on the development and implementation of machine learning models for dynamic risk prediction in a clinical context.

The course corresponds to 3 ECTS. View a more detailed description of the course.

Register for the course via moodle.

The course is directed at clinical PhD students and introduces how data science can be used to turn health data into clinically useful applications. The course will cover the full value chain of clinical data science, including the collection and management of health data, the application of these data as input for predictive models, and the implementation of predictive models in the clinic. The course provides an overview and understanding of the processes, rather than how to perform them. 
  
The course corresponds to 2.5 ECTS. View a more detailed description of the course.

Register for the course via moodle. 

The course is directed at PhD students working with translational and clinical cancer research. The course will cover several topics related to translational cancer research, including basic science methodologies, statistics and bioinformatics, and the key points in clinical trials. 
  
The course corresponds to 2 ECTS. View a more detailed description of the course.  

Register for the course via moodle. 

We offer supervision and co-supervision in data science for master and PhD students within the fields of biomedicine, bioinformatics, engineering and natural sciences, and medicine. 
  
If you are interested in doing a project with us, you are welcome to contact an employee.