First semester students in the Medical Informatics master's program: please see Prof. Pfeifer's Welcome message and useful information.

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Thank you for your interest in studying Medical Informatics at the University of Tübingen.

Our Master's degree program in Medical Informatics will be a perfect fit for you if you have a strong affinity for Computer Science and programming skills in general.

Your eligibility will depend on your previous qualifications in Computer Science and/or Medicine/Pharmacy. Knowledge in Biostatistics and Python would be particularly useful.

Language requirements:

• Our Master's degree program in Medical Informatics is an English language program, therefore Level B2 skills in English are required.
• A Level B2 certificate in German would only be required in case of attending certain eligible modules that are offered in German only.

Further details:

• Medical Informatics - Master
• FAQ Medical Informatics Master
• Recommended Course Plan.

For application deadlines, visit Application and Enrollment.

Online application for our Master’s programs: www.alma.uni-tuebingen.de/alma/pages/cs/sys/portal/hisinoneStartPage.faces.

We are looking forward to welcoming you soon as our student.

SUMMER 2023

Please check if your course takes place online or in presence, there might be changes during the semester.

• Preparatory Course in Programming for Medical/Life Scientists (March 30th - April 5th, 2023) for newly enrolled students in the Master's degree program in Medical Informatics with a background in Medicine/Pharmacy/Life Sciences.
   
• Lecture: Bioinformatics for Life Scientists
   
• Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
  This lecture provides an introduction into statistical machine learning with a focus on practical application in (biomedical) data analysis. It comprises basic methods for supervised (classification, regression) and unsupervised learning. Topics include but are not limited to: Linear models for regression and classification, model selection & regularization, cross validation, bootstrap, decision trees, random forest, boosting, support vector machines, dimensionality reduction, clustering methods. Programming tasks require submission in R.
   
• Lecture: Introduction to Cryptography
  Contact: mete.akguenspam prevention@uni-tuebingen.de
   
• Seminar: Machine Learning to Fight Infections
  This seminar covers different machine learning methods on biomedical data to answer medical questions of interest.

See also: IBMI Programming Helpdesk with focus on Java and Python.

WINTER 2022/2023

• Preparatory Course in Programming for Medical/Life Scientists for newly enrolled students in the Master's degree program in Medical Informatics with a background in Medicine/Pharmacy/Life Sciences.
   
• Lecture: Advanced Medical Informatics
  This lecture comprises different areas of Medical Informatics. The focus is on data integration, medical data privacy, artificial intelligence and data mining for health data, and treatment decision support systems. Course materials: https://ovidius.uni-tuebingen.de/ilias3/goto.php?target=crs_3885598&client_id=pr02
   
• Lecture: Medical Data Science
  This lecture comprises different areas of Medical Data Science. Data Science or statistical Machine Learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. To gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process. Specific topics are
  • Gaining new insights from medical data
  • Modeling uncertainty in medical data science models
  • Making medical findings available through interpretable decision support systems
    
    Method-wise, the lecture introduces methods for
  • GWAS analyses (e.g., LMMs),
  • sequence analysis (e.g., kernel methods),
  • small n problems (e.g., domain adaptation, transfer learning, and multitask learning),
  • data integration (advanced unsupervised learning methods),
  • learning probabilistic Machine Learning models (e.g., graphical models),
  • large data sets (e.g., deep learning models)
    
    Course materials: https://ovidius.uni-tuebingen.de/ilias3/goto.php?target=crs_3885597&client_id=pr02
     
• Seminar: Machine Learning for Health
  This seminar covers different machine learning methods on biomedical data to answer medical questions of interest.
   

SUMMER 2022

• Lecture: Bioinformatics for Life Scientists
• Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
• Lecture: Introduction to Cryptography
• Seminar: Computer Science Methods for Privacy Preservation and Personalized Medicine
  In this seminar, we discuss recently published research articles about ways to preserve privacy for sensitive data, privacy-preserving machine learning and personalized or precision medicine approaches.
   

WINTER 2021/2022

• Lecture: Advanced Medical Informatics
• Lecture: Medical Data Science
• Seminar: Machine Learning for Health
• Practical Course: Software Development with Scrum
   

SUMMER 2021

• Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
• Seminar: Machine Learning for Health
• Preparatory Course in Programming for Life Scientists
   

WINTER 2020/2021

• Lecture: Advanced Medical Informatics
• Lecture: Medical Data Science
• Seminar: Machine Learning for Health
   

SUMMER 2020

• Lecture: Medical Data Science
• Lecture: Introduction to Statistical Machine Learning for Bioinformaticians and Medical Informaticians
• Lecture: Bioinformatics for Life Scientists
• Seminar: Computer Science Methods for Privacy Preservation and Personalized Medicine
• Software Project in Medical Informatics
   

WINTER 2019/2020

• Lecture: Advanced Medical Informatics
• Practical Course: Machine Learning in Biomedicine
• Seminar: Machine Learning for Health
• Seminar: Biomedical Informatics Methods for Infection Research
   

SUMMER 2019

• Lecture: Medical Data Science
• Lecture: Bioinformatics for Life Scientists
• Seminar: Computer Science Methods for Privacy Preservation and Personalized Medicine
• Seminar: Probabilistic Graphical Models and Probabilistic Programming
• Software Project in Medical Informatics
   

WINTER 2018/2019

• Lecture: Advanced Medical Informatics
• Practical course: Machine Learning in Biomedicine
• Seminar: Machine Learning for Health
   

SUMMER 2018

• Lecture: Medical Data Science
• Lecture: Bioinformatics for Life Scientists
• Seminar: Computer Science Methods for Privacy Preservation in Biomedical Studies
• Software Project in Medical Informatics
   

WINTER 2017/2018

• Lecture: Advanced Medical Informatics
• Practical course: Machine Learning in Biomedicine
• Seminar: Machine Learning for Health
   

SUMMER 2017

• Lecture: Medical Data Science
• Seminar: Biomedical Informatics Methods for Infection Research
• Software Project in Medical Informatics

We are offering a range of topics for Master’s and Bachelor’s theses and research projects.

Feel free to check our group members’ research interests to find the most suitable contact person for your preferred field, or contact us to explore any specific areas for supervision.

You can download our Medical Informatics (or Bioinformatics) Master's Thesis Template here.

We are looking forward to discussing further details with you!