103 data-"https:"-"https:"-"https:"-"https:" positions at Technical University of Munich
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are
-
research ideas within the VIOLET research framework Work closely with clinicians and data scientists to ensure the developed systems meet clinical needs and are validated against real-world scenarios Mentor
-
. 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
-
and adults 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
-
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
-
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
-
15.09.2025, Wissenschaftliches Personal The Chair of Marketing Analytics, which is part of the Heilbronn Data Science Center and the TUM School of Management at the TUM Campus Heilbronn, is seeking
-
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
-
applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University
-
, 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