Emerging Frontiers of AI/HPC for Science

Emerging Frontiers of AI/HPC for Science

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講義科目

Graduate School of Information Science

1Year

KIIDD7MCA1

2.00Credits

講義 (Lecture)

Fall semester 2026

(Fall semester)

芝　隼人,Rashed Essam

情報科学研究科

English

日本語によるリアルタイム翻訳字幕の提供を検討中です。ただし、正確性は保証しません。

3／9

the classroom after the lecture, for 30 mins.

Course Coordinators:
    Hayato Shiba (shiba@gsis.u-hyogo.ac.jp)

    Essam Rashed (rashed@gsis.u-hyogo.ac.jp)           

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2◎／1〇

N/a

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[Course Objectives]
With the rapid advancement of AI, scientific simulation and research are undergoing a profound transformation. Across virtually all scientific domains, data from simulations and experiments are increasingly becoming targets of AI-driven analysis, and the very nature of how simulations and experiments are conducted is expected to change.   University of Hyogo will provide a comprehensive curriculum in AI/HPC for Science in the coming years. This course serves as the first step in that initiative and provides an overview of the emerging field of “AI-HPC for Science.”  

[Learning Outcomes]

• To master the fundamentals and applications of cutting-edge AI and HPC technologies recently introduced to the sciences.

• To acquire the skills necessary to explain these technological trends and integrate AI-HPC tools into individual research projects.

Keyword:  AI for Science, Big Data, Quantum Computing, Fugaku & Fugaku NEXT

In this lectures series,  experts relevant to AI/HPC for Science are invited to give lectures on advanced topics listed below:

1. An Invitation to AI-HPC for Science  (Satoshi Matsuoka, RIKEN R-CCS)
2. Integration of Simulation/Data/Learning and Beyond(Kengo Nakajima, RIKEN R-CCS) 
3. Scaling Artificial Intelligence on Supercomputers (Mohamed Wahib, RIKEN R-CCS)
4. Advancing System Software for AI4S, FugakuNEXT, and Future Supercomputing (Kento Sato, RIKEN R-CCS)
5. AI for Medical Imaging Science: From Image Acquisition to Clinical Reporting" (Essam Rashed, Univ. of Hyogo)
6. Quantum Computing and Quantum HPC Hybrid Platform (Tamiya Onodera, RIKEN R-CCS & Miwako Tsuji, Tsukuba Univ.) 
7. (TBD)

In-person

This course is broadcast live from the Kobe Campus for Information Science to the Kobe Campus for Commerce. Therefore, although the "Class Format" column indicates "In-person," students attending from the Kobe Syoka Campus will be participating via "Remote" learning.

Fully permitted

生成AIの利用にあたっては『本学の教育における生成AIの取扱いについて（学生向け）』の記載内容について留意すること。
この授業においては、授業内、予習復習、レポート等を含む成果物作成等において生成AIの利用を全面的に許可しており、生成AIの利用について制限を設けないが、生成AIによる出力結果をそのままレポートとして提出してはならない。
生成AIの出力した内容について、事実関係の確認や出典・参考文献を確認・追記することが重要である。
使用した場合にその旨をレポート等に記載するかどうか等については、担当教員の指示に従うこと。

指定しない

新しい研究分野についての講義であり、シラバスとしては記載しない。最新の文献が担当教員から適宜紹介される。

Review & Research Integration: 30 hours
　　　Reviewing lecture content and applying AI-HPC methods to individual research projects.

Report Writing: 15 hours
　　　Preparing and finalizing the required reports.

Active learning is not adopted.

Students who demonstrate understanding of the assigned tasks and complete the required reports will receive grades based on their level of proficiency in achieving the course objectives and learning outcomes. Grades will be assigned as follows: S (90 points or higher), A (80 points or higher), B (70 points or higher), and C (60 points or higher). Evaluation will be based on the assessment of the task reports. However, students who fail to submit assignments on three or more occasions will not be eligible for course credit.

Feedback on reports will be provided through the "Class Profile" feature of Universal Passport.

当科目は、博士前期課程・博士後期課程の両課程の学生が履修可能である。
博士前期課程にて本科目の単位取得した者は、博士後期課程博士後期課程において重複して単位取得することはできない。
該当する可能性がある者は、履修登録に際して十分に注意すること。

The reports should be submitted in Microsoft Office formats (docx) or PDF files through the Universal Passport.
In the event of any discrepancy between the English and Japanese versions, the English version shall prevail.