MSc in Computer Science

Faculty of Mechanical, Electrical Engineering and Informatics

Name of  qualification and level Duration of studies Intake Necessary no. credits for degree Tuition fee Application fee Supervisor Programme mentor
Computer Scientist (MSc) 4  semesters – full time programme September 120 credits 2,600 EUR / semester 100 EUR / application

Dr Zoltán Horváth

CV

Dr. Harmati István
harmati@sze.hu

The programme brings its active and motivated students to professional level in either artificial intelligence, computational modelling and high performance computing. The programme prepares the students to scientific research in international research and innovation teams, development work in industry, and/or professional work in industry and other sectors.


Structure of studies

Students have compulsory courses in the fields of basic and advanced mathematical technologies (numerical methods in linear algebra, differential equations, concept of digital twins), Python programming, cloud computing, high performance computing (HPC) and data science (data analysis, machine learning, neural networks). Elective subjects provide master-level knowledge in mathematical modelling, simulation and optimization, programming, and artificial intelligence.

A key element of the programme is the support of research and innovation activities. Students have a compulsory course in research methodology in the first semester, and can select Innovation and research communication courses as free elective subjects, which aim the preparation of a scientific publication.

Compulsory internship provides valuable work experience. Students can find positions in international research and innovation projects (see e.g. HiDALGO2- project), in university competence centres, or in companies in artificial intelligence, automotive industry and other sectors. Two semester thesis work can be linked with the internship via solving real industrial and research challenges with the support of internal and external supervisors.

The total number of credits needed to complete the programme is 120 credits. 1 credit equals one ECTS credit, and 1 credit is defined as 25 student working hours.


Language requirements

TOEFL 513 / IELTS 5.5 /oral examination, TOEFL IBT test score of 66 or any standardised international English language exam corresponding with Hungarian B2 at the Bachelors level and C1 at Masters level.

If there is no standardised English exam, the University relies on an online interview. Applicants not complying with language requirements must enrol in a preparatory language programme. After having completed the language course and passed the requisite final examination, if there are no professional reservations on the part of the Faculty, admission to the selected Bachelor's programme is automatic.


Academic requirements

Completed and signed application form, copy of your passport, notarized copy of Bachelor’s degree relevant* to the Master's studies, notarized copy of the Transcript of Records, notarized copy of your language certificate, recent photograph, Curriculum Vitae (resumé) in English, Motivational Letter in English, Academic Reference letter in English (Issued by your former academic institution/teacher)

*BSc either in Computer Science, Mathematics, Applied Mathematics, Informatics or Electrical Engineering


Who is the programme aimed at?

The MSc program is designed for students and professionals holding bachelor degrees (BSc) in informatics, applied mathematics, and some computation oriented engineering programmes. The program is designed to accommodate full-time students only.


Details

Compulsory courses: 85 credit points

Nr.

Neptun code of course

Name of course

lessons / week

seminars / week

laboratory / week

assessment type *

credit points

semester

pre-conditions

1

GKNM_MSTA025

Data analysis

4

0

0

v

4

1

-

2

GKNM_MSTA035

Digital twins

2

4

0

v

7

1

-

3

GKNM_MSTA036

Numerical linear algebra

2

2

0

v

5

1

-

4

GKNM_MSTA038

Python programming

2

4

0

v

7

1

-

5

GKNM_MSTA088

Introduction to HPC

2

2

0

v

5

1

-

6

GKNM_MSTA089

Research methodology

0

2

0

f

2

1

-

7

GKNM_MSTA039

High performance computing

2

2

0

v

5

2

Introduction to HPC

8

GKNM_MSTA040

Machine learning

2

2

0

v

5

2

Python programming

9

GKNM_MSTA044

Numerical methods for differential equations

2

2

0

v

5

2

Digital twins and Numerical linear algebra

10

GKNM_MSTA049

Neural networks

2

2

0

v

5

3

Machine learning

11

GKNM_MSTA090

Thesis consultation 1

0

0

0

f

5

3

Research methodology

12

GKNM_MSTA094

Professional Practice

0

0

0

a

0

3

-

13

GKNM_TATA051

Cloud computing

2

2

0

f

5

3

-

14

GKNM_MSTA091

Thesis consultation 2

0

0

0

f

25

4

Thesis consultation 1

Hungarian Language (compulsory)

Nr.

Neptun code of course

Name of course

lessons / week

seminars / week

assessment type *

credit points

1

KGNB_NOKA036

Hungarian Language & Culture 1

0

3

a

0

2

KGNB_NOKA037

Hungarian Language & Culture 2

0

3

a

0

 

Elective courses 
25 credit points
should be obtained from this group of courses.

Nr.

Neptun code of course

Name of course

lessons / week

seminars / week

Laboratory / week

assessment type* 

credit points

pre-conditions

1

GKNM_INTA056

Logic

2

2

0

v

5

-

2

GKNM_MSTA002

Theory of algorithms

2

2

0

v

5

-

3

GKNM_MSTA037

Nonlinear optimization

2

2

0

v

5

-

4

GKNM_MSTA041

Web technologies

2

2

0

v

5

-

5

GKNM_MSTA045

Linear Optimization

2

2

0

v

5

-

6

GKNM_MSTA047

Model order reduction

2

2

0

v

5

Numerical methods for differential equations

7

GKNM_MSTA048

Data assimilation

2

2

0

v

5

Data analysis

8

GKNM_MSTA050

Selected topics in machine learning

2

2

0

v

5

Machine learning

9

GKNM_MSTA092

Production software development

2

2

0

v

5

-

10

GKNM_TATA061

Digitalization for industry

2

2

0

v

5

-

  

Free optional courses 
10 credit points
should be obtained from this group of courses.

Nr.

Neptun code of course

Name of course

lessons /

week

seminars /

week

laboratory / week

assessment type *

credit points

pre-conditions

1

AJNM_JFTA005

Computational fluid dinamics in vehicle

engineering

2

2

0

f

5

Numerical methods for differential equations

2

AJNM_LSTA024

Logistics

2

2

0

v

6

-

3

GKNM_AMTA011

CAE Methods

2

1

0

v

5

-

4

GKNM_AUTA011

Automatic controls

2

0

0

v

5

-

5

KGNM_NETA028

Global economics

2

0

0

v

4

-

6

KGNM_NETA054

Advanced macroeconomics

2

0

0

v

4

-

7

KGNM_VKTA003

Leadership and Organizational Communication

2

2

0

v

5

-

8

KGNM_VKTA020

Innovation and Research Communication I.

0

0

0

f

5

-

9

KGNM_VKTA021

Innovation and Research Communication II.

0

0

0

f

5

-

 

* type of assessment
f - evaluation based on student’s performance and work during the semester
v - evaluation based on student’s exam grade in a 5-grade system:

 excellent (5) – good (4) – satisfactory (3) – passed (2) – fail (1)

 

Please find details of thesis and final exams on: https://math.sze.hu/en_GB/computer-science-msc

 

H-9026 Győr, Egyetem tér 1. 

Hungary

(Administration Building 103.)

0036/96/613-700, 0036/503-419

international@sze.hu


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