Fizikus Mérnök BSc kurzus lista (angol)

Physicist-Engineer Responsible: Dr. András Halbritter
Legend
Type, C - compulsory, E - elective modules, O - optional
Grading, e - examination, c - coursework grade, a - audit
# - Proposed semester, *proposed semester of elective courses, @-alternative semester




Subject Code Type Eval Credit Hours Proposed Semester Prerequisites
Lect Prac Lab 1 2 3 4 5 6 7
COMPULSORY COURSES 154


Basic Knowledge 6


Physics Problem Solving Tutorial
BMETE15AP58
C c 6 0 4 0 #





-
Physics 34


Mechanics
BMETE11AP59
C e 8 4 2 0
#




Physics Problem Solving Tutorial
Electrodynamics and Optics
BMETE12AP49
C e 8 4 2 0

#



Mechanics
Thermodynamics and Statistical Physics
BMETE11AP60
C e 6 2 2 0


#


Mechanics
Modern Physics
BMETE15AP59
C e 8 4 2 0


#


Mechanics
Solid State Physics
BMETE11AP61
C e 4 2 1 0



#

Modern Physics
Mathematics 34


Vector and Matrix Algebra
BMETE91AP62
C e 8 4 2 0 #





-
Calculus
BMETE92AP61
C e 8 4 2 0 #





-
Multivariable Calculus
BMETE92AP62
C e 8 4 2 0
#




Calculus
Probability theory
BMETE95AP45
C e 5 2 2 0

#



Multivariable Calculus
Mathematical Methods in Physics
BMETE11AP58
C e 5 2 2 0

#



Vector and Matrix Algebra, Multivariable Calculus
Informatics and Computing 23


Programming 1
BMEVIAUAA00
C c 7 2 2 2 #





-
Programming 2
BMEVIAUAA01
C c 6 2 0 2
#




Programming 1
Introduction to Numerical Algorithms
BMETE15AP60
C c 6 2 0 2

#



Vector and Matrix Algebra, Programming 1
Introduction to Machine Learning
C c 4 1 0 2


#


Introduction to Numerical Algorithms
Applied Science, Technology and Design 47


Data Collection and Evaluation
BMETE11AP63
C c 7 0 0 4
#




-
Measurement Design and Laboratory Exercises
BMETE11AP64
C c 3 0 0 2

#



Data Collection and Evaluation
Measurement Techniques
BMETE11AP65
C e 3 2 0 0

#



-
Measurement Techniques Laboratory
BMETE11AP66
C c 5 0 0 3


#


Measurement Techniques, Meas. Design and Lab. Exercises
Computer Controlled Measurements
BMETE11AP67
C c 3 0 0 2


#


Programming 1
Computer Aided Design
C c 4 1 0 2



#

-
Electronics
C c 4 2 1 0


#


-
Electronics Laboratory
C c 3 0 0 2



#

Electronics, Data Collection and Evaluation
Technical chemistry
C c 3 2 0 0



#

-
Radiation Protection
C e 3 2 0 0



#

Modern Physics
Elective Subjects on Engineering (9 credits from the list below. Asterisked (*) courses can be taken in the Nuclear Technologies and Sustainable Energetics specialization, if not taken here.)
E
3 1 1 0



#



E
6 3 2 0




#


Renewable energy sources
E c 4 3 0 1



*

Thermodynamics and Statistical Physics
Life cycle assessment
E e 3 2 0 1




*
-
Electric power transmission
E e 4 2 1 0



*

Electronics
Fluid Mechanics
E c 6 2 2 1



@ * @ Mechanics, Multivariable Calculus
Numerical modelling of fluid flows
E c 4 1 0 2




*
Multivariable Calculus
Advanced Thermodynamics
E c 4 2 1 0



*

Thermodynamics and Statistical Physics
Control engineering
E e 5 2 1 1




*
Measurement Techniques, Electronics, Mathematical Methods in Physics
Industrial control
E e 4 2 1 0





* Control Eng., Programming 2, Measurement Techniques
Management and Communication 10


Elective Block 1 (2 credits from the list below)
E c 2 0 2 0 #







English for University Studies, English B2+
E c 2 0 2 0 *





-
Communication Skills, English B2
E c 2 0 2 0 *





-
English for Engineers, English B2
E c 2 0 2 0 *





-
Professional Writing, English C1*
E c 2 0 2 0 *





-
English for Professional Success, English C1*
E c 2 0 2 0 *





-
Elective Block 2 (6 credits from the list below, or the asterisked (*) courses in elective block 1, if not taken there)
E c 6 3 2 0




#


Economics 1
E c 2 2 0 0



*
@ -
Management and Business Economics
E c 3 2 0 0




*
-
Cross-cultural Communication, English B2
E c 2 0 2 0




*
-
Psychology
E e 3 2 0 0




*
-
Ergonomics
E c 2 2 0 0



@ * @ -
The Economic law of the European Union
E c 3 2 0 0



@ * @ -
Corporate law
E e 3 2 0 0



*
@ -
Safety Culture
E c 2 0 2 0




*
-
Presentation Skills
C c 2 0 2 0





# -
THESIS WORK

Thesis work
C c 15








# >=150 credits
INTERNSHIP

6 weeks internship after semester 6
C a 0








# Must have selected specialization


SUMMED CREDITS 31 29 30 30 20 12 17
CONTACT HOURS (lecture) 10 10 12 9 8 6 0
CONTACT HOURS (practice)









12 4 6 5 2 4 2
CONTACT HOURS (laboratory)









2 6 4 7 4 0 0
CONTACT HOURS (TOTAL) 24 20 22 21 14 10 2
NUMBER OF EXAMS 2 2 4 2 2 0 0
NUMBER OF COURSEWORK GRADES 3 2 2 4 3 1 1


OPTIONAL COURSES (from the list below, or any BME course) 10




0 6 4


Nobel Prize Physics in Everyday Application – Laboratory Exercise
BMETE11AP68
O c 3 0 2 0 #





-


Physicist-Engineer Project Work 1
BMETE15AP62
O c 3 0 2 0 #





-


SPECIALIZATIONS 31




10 12 9

Nanotechnology and Quantum Applications, Specialization selection criteria:
>=90 credits, Modern Physics


TOTAL NUMBER OF CREDITS 210
31 29 30 30 30 30 30

Physicist-Engineer - Nanotechnology and Quantum Applications Specialization
Specialization Responsible: Dr. Szabolcs Csonka
Legend
Type, C - compulsory, E - elective modules, O - optional
Grading, e - examination, c - coursework grade, a - audit
# - Proposed semester, *-propsed semster of courses in elective block, @-alternative semester



Subject Code Type Eval Credit Hours Proposed Semester Prerequisites
Lect Prac Lab 1 2 3 4 5 6 7
COMPULSORY COURSES 18


Advanced Physics Course (2 subjects from the courses below)
E e 6 2 0 0



@ #


Advanced Quantum Physics FI-EFT E e 3 2 0 0




*
Modern Physics
Introduction to Semiconductor Physics, Nanophysics and Magnetism FI-FT E e 3 2 0 0




*
Solid State Physics
Optics FI-AFT E e 3 2 0 0



*

Electrodynamics and Optics
Nanotechnology and Quantum Applications Specialization Laboratory 1 FI-FT C c 6 0 0 4



#

Measurement Design and Laboratory Exercises
Nanotechnology and Quantum Applications Specialization Laboratory 2 FI-FT C c 6 0 0 4




#
Nanotechnology and Quantum Applications Specialization Laboratory 1
ELECTIVE COURSES 42


Quantum Information Processing

Introduction to Quantum Computing and Communications VIK E c 2 2 0 0



#
@ Vector and Matrix Algebra, Modern Physics
Quantum Information Processing FI-EFT E e 3 2 0 0




#
Modern Physics
Advanced Quantum Mechanics Problem Solving FI-FT E c 3 0 2 0




#
Modern Physics, parallel: Adv. Quant. Mechanics
Artificial Inteligence in Data Science FI-FT E c 5 1 2 0



@
# Introduction to Numerical Algorithms
Nanotechnology, Semiconductor Technology, Materials Science

Advanced Semiconductor Devices FI-FT E e 3 2 0 0




#
Solid State Physics
Advanced Micro and Nanoscale Material Processing and Analysis Techniques FI-AFT E c 3 2 0 0




#
Modern Physics
Spectroscopic Methods in Material Science FI-AFT E e 3 2 0 0



#
@ Modern Physics
Chemical methods in nanotechnology FI-FT E e 3 2 0 0



@
# Technical chemistry
Optics and Laser Technology

Optics problem solving FI-AFT E c 2 0 2 0



#
@ Electrodynamics and Optics, parallel: Optics
Laser technology FI-AFT E c 3 2 0 0



#
@ Electrodynamics and Optics
Microscopy FI-AFT E c 3 2 0 0




#
Electrodynamics and Optics
Optical Metrology FI-FT E e 3 2 0 0




#
Electrodynamics and Optics
Further Elective Courses

Measurement Control Project Work in LabVIEW Environment FI-FT E c 3 0 0 2



#

Programming 1
Data Science Aided Measurements FI-FT E c 3 0 0 2




#
Intr. to Machine Learning, Meas. Tech. Laboratory




































Physicist-Engineer - Nuclear Technologies and Sustainable Energetics Specialization
Specialization Responsible: Szieberth Máté
Legend
Type, C - compulsory, E - elective modules, O - optional
Grading, e - examination, c - coursework grade, a - audit
# - Proposed semester, *-propsed semster of courses in elective block, @-alternative semester



Subject Code Type Eval Credit Hours Proposed Semester Prerequisites
Lect Prac Lab 1 2 3 4 5 6 7
COMPULSORY COURSES 12











Nuclear technology and sustainable energies laboratory 1
C c 6 0 0 4



#

Modern Physics, Measurement Techniques, Meas. Design and Lab. Exercises
Nuclear technology and sustainable energies laboratory 2
C c 6 0 0 4




#
Nuclear technology and sustainable energies laboratory 1
ELECTIVE COURSES (The asterisked courses (*) from the Elective Subjects on Engineering elective module in the main curriculum can be also taken if not taken there) 19











Sustainable Development and Energetics BMETE80AF06 E c 3 2 0 0




#
Modern Physics
Radioactive Waste Management BMETE80AF10 E c 3 2 0 0




#
Radiaton Protection
Radiochemistry and Nuclear Chemistry BMETE80AF32 E c 4 3 0 0




#
Radiaton Protection
Introduction to CFD Methods BMETE80AF37 E c 4 1 0 2




#
Multivariable Calculus
Monte Carlo Methods BMETE80AF45 E e 4 2 1 0



#

Probability theory
Radiation Detection and Measurement BMETE80AF42 E e 3 2 0 0




#
Modern Physics, Measurement Techniques
Medical Imaging Systems BMETE80AF35 E c 3 2 0 0




#
Modern Physics, Measurement Techniques
Introduction to Fusion Plasma Physics BMETE80AF36 E e 3 2 0 0



#
@ Multivariable Calculus, Modern Physics
Nuclear Safety BMETE80AF30 E c 3 2 0 0



#
@ Modern Physics
Thermal Hydraulics of Nuclear Power Plants BMETE80AF31 E c 5 3 1 0



#
@ Modern Physics, Thermodynamics and Statistical Physics
Reactor Physics BMETE80AF33 E e 5 3 1 0



#
@ Modern Physics




































Physicist-Engineer - Scientific Data Processing Specialization
Specialization Responsible: Dr. János Török
Legend
Type, C - compulsory, E - elective modules, O - optional
Grading, e - examination, c - coursework grade, a - audit
# - Proposed semester



Subject Code Type Eval Credit Hours Proposed Semester Prerequisites
Lect Prac Lab 1 2 3 4 5 6 7
COMPULSORY COURSES




21


Introduction to Experimental Data Handling
C e 3 2 0 0



#

Probability theory
Complex networks
C e 4 2 1 0



#

Probability theory
Introduction to Data Science
C e 4 3 0 1




#
Intr. to Exp. Data Handl., Intr. to Machine Learning, Compl. Netw.
Programming Exercises for Data Science
C c 2 0 1 0




#
parallel: Introduction to Data Science
Data Science Aided Measurements
C c 3 0 0 2




#
Intr. to Machine Learning, Meas. Tech. Laboratory
Artificial Inteligence in Data Science
C c 5 1 2 0





# Introduction to Machine Learning
ELECTIVE COURSES 10


Data-driven and agent-based modeling
E c 4 1 0 2




#
Intro. to Machine Learning, Intro. to Exp. Data Handling
Monte Carlo Methods
E e 4 2 1 0



#

Probability theory
Measurement Control Project Work in LabVIEW Environment
E c 3 0 0 2



#

Programming 1
The Fundaments and Applications of Finite Element Modeling
E c 3 0 0 2





# Electrodynamics and Optics, Multivariable Calculus
Medical Imaging Systems
E c 3 2 0 0




#
Modern Physics, Measurement Techniques