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 |
Dr. Harmati István |
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 |
Data analysis |
4 |
0 |
0 |
v |
4 |
1 |
- |
|
2 |
Digital twins |
2 |
4 |
0 |
v |
7 |
1 |
- |
|
3 |
Numerical linear algebra |
2 |
2 |
0 |
v |
5 |
1 |
- |
|
4 |
Python programming |
2 |
4 |
0 |
v |
7 |
1 |
- |
|
5 |
Introduction to HPC |
2 |
2 |
0 |
v |
5 |
1 |
- |
|
6 |
Research methodology |
0 |
2 |
0 |
f |
2 |
1 |
- |
|
7 |
High performance computing |
2 |
2 |
0 |
v |
5 |
2 |
Introduction to HPC |
|
8 |
Machine learning |
2 |
2 |
0 |
v |
5 |
2 |
Python programming |
|
9 |
Numerical methods for differential equations |
2 |
2 |
0 |
v |
5 |
2 |
Digital twins and Numerical linear algebra |
|
10 |
Neural networks |
2 |
2 |
0 |
v |
5 |
3 |
Machine learning |
|
11 |
Thesis consultation 1 |
0 |
0 |
0 |
f |
5 |
3 |
Research methodology |
|
12 |
Professional Practice |
0 |
0 |
0 |
a |
0 |
3 |
- |
|
13 |
Cloud computing |
2 |
2 |
0 |
f |
5 |
3 |
- |
|
14 |
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 |
Hungarian Language & Culture 1 |
0 |
3 |
a |
0 |
|
2 |
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 |
Logic |
2 |
2 |
0 |
v |
5 |
- |
|
2 |
Theory of algorithms |
2 |
2 |
0 |
v |
5 |
- |
|
3 |
Nonlinear optimization |
2 |
2 |
0 |
v |
5 |
- |
|
4 |
Web technologies |
2 |
2 |
0 |
v |
5 |
- |
|
5 |
Linear Optimization |
2 |
2 |
0 |
v |
5 |
- |
|
6 |
Model order reduction |
2 |
2 |
0 |
v |
5 |
Numerical methods for differential equations |
|
7 |
Data assimilation |
2 |
2 |
0 |
v |
5 |
Data analysis |
|
8 |
Selected topics in machine learning |
2 |
2 |
0 |
v |
5 |
Machine learning |
|
9 |
Production software development |
2 |
2 |
0 |
v |
5 |
- |
|
10 |
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 |
Computational fluid dinamics in vehicle engineering |
2 |
2 |
0 |
f |
5 |
Numerical methods for differential equations |
|
2 |
Logistics |
2 |
2 |
0 |
v |
6 |
- |
|
3 |
CAE Methods |
2 |
1 |
0 |
v |
5 |
- |
|
4 |
Automatic controls |
2 |
0 |
0 |
v |
5 |
- |
|
5 |
Global economics |
2 |
0 |
0 |
v |
4 |
- |
|
6 |
Advanced macroeconomics |
2 |
0 |
0 |
v |
4 |
- |
|
7 |
Leadership and Organizational Communication |
2 |
2 |
0 |
v |
5 |
- |
|
8 |
Innovation and Research Communication I. |
0 |
0 |
0 |
f |
5 |
- |
|
9 |
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