Overview
Munich School for Data Science Germany PhD Scholarship 2025 (Fully Funded) is available to undertake PhD level programs at Munich School for Data Science.
Description
Munich School for Data Science Germany PhD Scholarship 2025 (Fully Funded) is available to undertake PhD level programs at Munich School for Data Science.
Eligibility
Candidates must have:
A strong academic background in Computer Science, Data Science, or similar fields;
Or a degree in domain sciences (Life Sciences, Medicine, Health, Earth Observation, Robotics) with a heavy focus on data science;
Or a degree in an interdisciplinary field such as Geoinformatics or Bioinformatics.
Candidates should have a deep interest in developing and applying state-of-the-art data science techniques to real-world scientific challenges.
Additional Information
Degree Level
Munich School for Data Science Germany PhD Scholarship 2025 (Fully Funded) is available to undertake PhD level programs at Munich School for Data Science.
Available Subjects
PhD students will engage in advanced research in areas such as:
Large-Scale Data Management
Machine Learning and Deep Learning
High-Performance Computing and Analytics
Data Mining and Data Integration
Uncertainty Quantification and Model-Order Reduction
Multi-Fidelity Methods
Applications in Life Sciences, Medical Research, Environmental Monitoring, and Robotics
Benefits
Fully funded PhD position with a salary contract (not just a stipend), including health insurance and social security benefits.
Top-tier research environment across leading institutions like Helmholtz Munich, DLR, TUM, and LMU.
Structured training with scientific workshops, summer schools, and transferable skills development.
Funded international research stays to build global experience and networks.
Real-world application of data science methods in fields like health, earth observation, robotics, and life sciences.
Strong career development support through coaching, scientific writing, project management, and industry networking.
Interdisciplinary research opportunities combining computer science with medicine, biology, environmental science, and engineering.
Prestigious academic qualification recognized globally, opening doors in academia, research, and industry.
Eligible Nationalities
Open to applicants of all nationalities.
Eligibility Criteria
Candidates must have:
A strong academic background in Computer Science, Data Science, or similar fields;
Or a degree in domain sciences (Life Sciences, Medicine, Health, Earth Observation, Robotics) with a heavy focus on data science;
Or a degree in an interdisciplinary field such as Geoinformatics or Bioinformatics.
Candidates should have a deep interest in developing and applying state-of-the-art data science techniques to real-world scientific challenges.
Application Procedure
Application Deadline: May 6, 2025
Application Mode: Online submission through the MUDS official application portal (link based on general format; please confirm on MUDS site)
Selection Process: Evaluation based on academic excellence, research potential, and motivation.
Interested candidates are encouraged to apply early to maximize their chances.
Why Pursue A Phd At Muds
Interdisciplinary Focus: Bridging computer science, data science, life sciences, medicine, earth observation, and robotics.
Cutting-Edge Research: Topics range from large-scale data management, data mining, machine learning, deep learning, high-performance computing, data integration, uncertainty quantification, to multi-fidelity modeling.
Comprehensive Training: Includes workshops, summer schools, international research stays, and transferable skills development.
World-Class Institutions: Collaborate with Germany’s leading universities and research centers.
International Exposure: MUDS fosters global collaboration and aims to produce internationally competitive data scientists.
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Card
Funding
Fully Funded
Organization
Munich School for Data Science
Degree
PhD
Subjects
Data Science
Nationality
International Students
Country
Germany
Source
Information provided by scholarships_ads.