COS 500: Frontiers & Methodologies in Computational Sciences
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Course Title |
Frontiers & Methodologies in Computational Sciences |
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Course Code |
COS 500 |
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Course Type |
Mandatory |
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Level |
PhD |
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Instructor’s Name |
Prof. Constantine Dovrolis (Lead Instructor), Prof. Constantina Alexandrou, Prof. Vangelis Harmandaris, Assoc. Prof. Theodoros Christoudias, Assoc. Prof. Giannis Koutsou, Assoc. Prof. Mihalis Nicolaou, Dr. Simone Bacchio, Dr. Leonidas Christodoulou, Dr. Kyriaki Kilili, Dr. Andreas Athenodorou, Dr. Kostas Blekos, Dr. Ferenc Pittler, Dr. Andreas Demou |
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ECTS |
10 |
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Lectures / week |
2 (90 min each) |
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Laboratories / week |
- |
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Course Purpose and Objectives |
The course objective is to expose students to frontier research in High Performing Computing, Data Science Methodologies and Artificial Intelligence on a seminarbased structure. It aims to train students to overview the literature connected to a research of their interest, to read and understand research articles and to present them to their peers. Students will develop their communication skills, share their findings with their peers and develop awareness on a range of relevant topics, including: evaluating new algorithms and methodologies, code optimization and data-management strategies, novel computing architectures, application of HPC and machine/deep learning in solving complex problems from physics, biology, chemistry, finances and engineering. |
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Learning Outcomes |
By the end of the course students will: (i) Reconstruct, analyze, critically evaluate and synthesize information and results presented in technical and scientific journals
(ii) Become adept in applying principles of frontier research on HPC and Data Science methodologies to solve complex problems from a wide range of fields.
(iii) Develop skills in designing and delivering research seminars
(iv) Engage in scientific discord with their peers
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Prerequisites |
None |
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Background Requirements |
None |
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Course Content |
The topics will be in computational sciences and will be selected by the students in consultation with the instructors. They will range from high performance computing and data science algorithms and mathematical modelling to scientific application from physics, engineering, earth system science, life sciences, finances and cultural heritage.
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Teaching Methodology |
- 14 x 3 hours Seminars
- Review of literature and reading of scientific publications on specific topic
- Presentation of a research topic
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Bibliography |
Published research articles in peer reviewed journals |
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Assessment |
The assessment will be based on:
- Seminar presentation in class
- Participation and active engagement in seminars
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Language |
English |
