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Study Computational Neuroscience in Bremen (2026/27)
Studying Computational Neuroscience in Germany as an international student offers rigorous training at the intersection of neuroscience, mathematics, and data science. Germany is internationally recognized for its strength in theoretical and computational brain research, with access to high-performance computing, advanced neuroimaging data, and interdisciplinary research centers. International students benefit from a strong research culture and close collaboration across disciplines, preparing them to model, analyze, and understand complex neural systems in health and disease.
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Most Popular Computational Neuroscience Study Programs in Bremen
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Fees & Costs
German Students 
Other EU Students 
Non-EU Students German StudentsDegree
Tuition Fee
Master
No tuition fees
Disclaimer: You should use this text as a general guide, but it cannot serve as a guarantee. Keep in mind that there is no universal tuition fee that applies to all study programs and all student groups. Hence, we urge you to always individually double-check the specific fees of the specific study programs on their official websites.
Deadlines
Below are the most frequent application deadlines for Computational Neuroscience programs in Bremen.
German Students Other EU Students 
Non-EU Students Non-EU StudentsWinter Semester
Summer Semester
| Admission-restricted courses | Admission-free courses | |
|---|---|---|
| Master | 30 April | No courses |
Master
Admission-restricted courses
30 April
Admission-free courses
No courses
Specific courses may have different application deadlines. Please always double-check the information individually. 
Numerus Clausus (Admission Restriction)
For degree courses with a numerus clausus, part of the study places are allocated according to the grade point average (GPA) of the previous degree. Selection is generally based on how many applicants apply for a place. The higher the grade, the more difficult it is to get onto the course. The German grading system ranges from 1.0 (very good) to 6.0 (unsatisfactory).
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