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EUMaster4HPC

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EUMaster4HPC

EUMaster4HPC is an HPC European consortium leading educational activities, funded by the EuroHPC Joint Undertaking to design and implement the first pan-European High Performance Computing (HPC) Master programme…>

Information on EUMaster4HPC

By bringing together major players in HPC education in Europe, EUMaster4HPC unifies existing programmes into a common pan-European curriculum to educate students in areas such as the design, deployment, operation, and/or the use of current and future generation HPC and HPC-related technologies in Europe.

EUMaster4HPC emphasises collaboration across Europe with innovative teaching paradigms such as co-teaching and strong connections with the industry and the best specialists in HPC education to develop the most relevant and up-to-date teaching contents. This unique pan-European curriculum will provide a coordinated approach to HPC education and training in Europe that could be adopted by other universities. The creation of a European network of HPC specialists will strengthen mobility and mutual support between students, teachers and industrial experts. A particular focus on mobility of students and teachers will enable students to rapidly gain experience through internships and exposure to European supercomputing centres.

Students can apply online through the Application form.

Please find a Standard Study Plan for the High-Performance Computing Master’s program at Friedrich-Alexander University (FAU) and the General Module Catalog for the Winter Semester 2024/25. This program is presently integrated into the Computational Engineering Master´s program.

 

EUMaster4HPC Standard Study Plan

EUMaster4HPC
Standard study plan for master students
1st year 2nd year
7 8 9 10
Computer Science – min. 40 ECTS ECTS total
Architectures of Supercomputers W  5
Practical Parallel Algorithms with MPI W 5
Advanced Programming Techniques W 7,5
Programming Techniques for Supercomputers S  7,5  
High End Simulation in Practice S 7,5
Computer Architecture W 7,5 40
Mathematics – min. 20 ECTS
Functional Analysis for Engineers W 5
Optimization for Engineers S 7,5
Algorithms of Numerical Linear Algebra W 7,5 20
TAF – min. 20 ECTS
Pattern Recognition W 5
Deep Learning S 5
Fundamentals in Anatomy and Physiology for Engineers S 5
Machine learning for time series W  5
HPC Project W 5 5
Seminar, Referat und Masterarbeit – 30 + 5 ECTS  5 30 35
total 27,5 32,5 30 30 120

Module Catalog

Winter semester 2024/25

Friedrich-Alexander-Universität
Erlangen-Nürnberg

Schlossplatz 4
91054 Erlangen
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