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COS 500: Frontiers & Methodologies in Computational Sciences

 

Course Title

Frontiers & Methodologies in Computational Sciences

Course Code

COS 500

Course Type

Mandatory

Level

PhD

Instructor’s Name

Prof. Vangelis Harmandaris (Lead Instructors), Prof. Constantina Alexandrou, Prof. George Christophides, Prof. Costas Papanicolas, Asst. Prof. Theodoros Christoudias, Assoc. Prof. Giannis Koutsou, Asst. Prof. Mihalis Nikolaou, Asst. Prof. Nikos Savva, Asst. Prof. George Artopoulos, Dr. Simone Bacchio, Dr. Charalambos Chrysostomou, Dr. Jacob Finkenrath, Dr. Kyriakos Hadjiyiannakou, Dr. Stefan Kühn

ECTS

10

Lectures / week

2 (90 min each)

Laboratories / week

-

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.

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

Prerequisites

None

Background Requirements

None

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.

Teaching Methodology

-   14 x 3 hours Seminars
-   Review of literature and reading of scientific publications on specific topic
-   Presentation of a research topic

Bibliography

 Published research articles in peer reviewed journals

Assessment

 The assessment will be based on:
-   Seminar presentation in class
-   Participation and active engagement in seminars

Language

English