84 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"SciLifeLab" positions at Technical University of Munich in Germany
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
immediately and continue until the position is filled. Preference will be given to applications received by January 15, 2026. Only shortlisted candidates will be contacted. Data Protection Information: When you
-
. Publish and present findings in international journals and conferences. Supervise and mentor junior researchers and students. Profile PhD in Computer Science, Biomedical Engineering, Data Science, or a
-
guidelines into formal ontologies and knowledge graphs Design and develop modules for Medical Informatics Initiative (MII)/FHIR interoperability of the research prototype Work closely with clinicians and data
-
performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take
-
of entrepreneurship and/or family business, • possesses solid knowledge of empirical research methods, ideally in quantitative, qualitative, or experimental research designs, • demonstrates proficiency in data
-
actively seek, select, and evaluate information to learn about the world. This is an open-topic position for doctoral or postdoctoral researchers who wish to pursue their own research ideas within the broad
-
consideration, the application should include a cover letter, a detailed CV, a brief statement of your research experiences and interests, relevant certificates, and the names and contact information of two
-
accessible to users from science and industry Your qualifications: ■ Master’s or equivalent graduate degree in computer science, artificial intelligence, machine learning, mathematics, statistics, data science
-
young scientists worldwide to German research institutions. For more information, see here . The position is suitable for disabled persons. Disabled applicants will be given preference in case
-
, static user representations, and data sparsity. While deep learning models offer improvements, they often come with high computational costs and require frequent retraining, which limits their scalability