Fellowship goal is to model, predict, and find causal factors related to the outcomes of interest, such as length of stay, prolonged intubation, blood transfusion, and renal failure.
The Technical University of Munich (TUM) is one of Europe’s top universities. It is committed to excellence in research and teaching, interdisciplinary education and the active promotion of promising young scientists. The university also forges strong links with companies and scientific institutions across the world. TUM was one of the first universities in Germany to be named a University of Excellence. Moreover, TUM regularly ranks among the best European universities in international rankings.
- Applications Deadline: Open for applications
- Course Level: Fellowship is available to study Postdoctoral or PhD programs.
- Study Subject: Fellowship is awarded in Machine Learning for Patient Data Analysis.
- Nationality: Fellowship is available for International students.
- Number of Scholarships: Numbers not given
- Scholarship can be taken in Germany
Eligibility for the Scholarship
Eligible Countries: Fellowship is available for International students.
Entrance Requirements: Applicants must meet the following criteria:
– MSc or PhD degree in computer science, statistics, mathematics, data science or equivalent
– Strong background in machine learning (graphical models, Bayesian and neural networks), statistics, and preferably causal inference methods;
– Knowledge of and/or experience with time-series data, preferably clinical data;
– Programming expertise in Python, R, and SQL;
– Interest and/or experience in working with healthcare problems (particularly surgical procedures);
– Demonstrated skill in scientific writing;
– Excellent interpersonal skills with the ability to work independently and in collaboration with a multidisciplinary team of surgeons and engineers.
– Experience with healthcare data and building real-world systems is a plus.
English Language Requirements: Applicants whose first language is not English are usually required to provide evidence of proficiency in English at the higher level required by the University.
How to Apply: Interested individuals should send a cover letter, a CV and contact information for two references to Dr. Narges Ahmidi (narges.ahmidi-at-helmholtz-muenchen.de).