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The Applied Mathematics and Statistics Ph.D program practiced by the Department of Mathematics enables the students to conduct advanced research for application in the fields of Engineering, Medicine, Physics, Biology, Computer Sciences under the supervision of advisors. The academic staff comprises dynamic lecturers who are capable of conducting efficient scientific research and publishing them in internationally recognized journals. The Applied Mathematics and Statistics Ph.D program is designed to enable the graduates to have a successful academic career and carry out scientific research.
To see the course details (such as objectives, learning outcomes, content, assessment and ECTS workload), click the relevant Course Code given in the table below.
| 1. Year Fall Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| ELEC 001 | Elective Course I | 3 | 0 | 3 | 7.5 | |
| MATH 505 | Advanced Mathematical Analysis | 3 | 0 | 3 | 7.5 | |
| MATH 601 | Differential Equations | 3 | 0 | 3 | 7.5 | |
| STAT 601 | Probability Theory and Mathematical Statistics | 3 | 0 | 3 | 7.5 | |
| Total : | 30 |
| 1. Year Spring Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| ELEC 002 | Elective Course II | 3 | 0 | 3 | 7.5 | |
| ELEC 003 | Elective Course III | 3 | 0 | 3 | 7.5 | |
| GSNS 695 | Seminar | 0 | 0 | 0 | 7.5 | |
| MATH 602 | Advanced Linear Algebra and Optimization | 3 | 0 | 3 | 7.5 | |
| Total : | 30 |
| 2. Year Fall Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| GSNS 697 | Individual Studies in Applied Mathematics and Statistics | 0 | 0 | 0 | 30 | |
| Total : | 30 |
| 2. Year Spring Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| GSNS 698 | Thesis Proposal in Applied Mathematics and Statistics | 0 | 0 | 0 | 30 | |
| Total : | 30 |
| 3. Year Fall Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| GSNS 699 | Thesis | 0 | 0 | 0 | 30 | |
| Total : | 30 |
| 3. Year Spring Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| GSNS 699 | Thesis | 0 | 0 | 0 | 30 | |
| Total : | 30 |
| 4. Year Fall Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| GSNS 699 | Thesis | 0 | 0 | 0 | 30 | |
| Total : | 30 |
| 4. Year Spring Semester | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| GSNS 699 | Thesis | 0 | 0 | 0 | 30 | |
| Total : | 30 |
| Elective Courses | ||||||
| Code | Pre. | Course Name | Theory | Application/Laboratory | Local Credits | ECTS |
| ECON 517 | Financial Econometrics | 3 | 0 | 3 | 7.5 | |
| FM 506 | Stochastic Processes in Finance | 3 | 0 | 3 | 7.5 | |
| IE 502 | Probabilistic Systems Analysis | 3 | 0 | 3 | 7.5 | |
| IES 503 | Artificial Intelligence | 3 | 0 | 3 | 7.5 | |
| IES 508 | System Simulation and Modeling | 3 | 0 | 3 | 7.5 | |
| IES 509 | Heuristics | 3 | 0 | 3 | 7.5 | |
| IES 511 | Machine Learning | 3 | 0 | 3 | 7.5 | |
| IES 513 | Mathematical Programming and Applications | 3 | 0 | 3 | 7.5 | |
| IES 534 | Nonlinear Programming | 3 | 0 | 3 | 7.5 | |
| IES 570 | Criptology and Computer Security | 3 | 0 | 3 | 7.5 | |
| MATH 504 | Statistics | 3 | 0 | 3 | 7.5 | |
| MATH 508 | Partial Differential Equations | 3 | 0 | 3 | 7.5 | |
| MATH 552 | Copula Theory and Its Application in Finance | 3 | 0 | 3 | 7.5 | |
| MATH 553 | Optimization | 3 | 0 | 3 | 7.5 | |
| MATH 554 | Basic Topics in Mathematics | 3 | 0 | 3 | 7.5 | |
| MATH 555 | Financial Mathematics | 3 | 0 | 3 | 7.5 | |
| MATH 600 | Mathematics Softwares and Research Methods | 3 | 0 | 3 | 7.5 | |
| MATH 654 | Discrete Optimization and Heuristic Methods | 3 | 0 | 3 | 7.5 | |
| MATH 655 | Fuzzy Set Theory and Its Applications | 3 | 0 | 3 | 7.5 | |
| MATH 656 | Complex Analysis | 3 | 0 | 3 | 7.5 | |
| MATH 657 | Time Scales | 3 | 0 | 3 | 7.5 | |
| MATH 658 | Data Analysis with Mathematica | 3 | 0 | 3 | 7.5 | |
| MATH 659 | Graph Theory | 3 | 0 | 3 | 7.5 | |
| MATH 660 | Algebraic Geometry | 3 | 0 | 3 | 7.5 | |
| MATH 661 | Finite Fields and Its Applications | 3 | 0 | 3 | 7.5 | |
| MATH 662 | Cryptography | 3 | 0 | 3 | 7.5 | |
| MATH 663 | Biomathematics | 3 | 0 | 3 | 7.5 | |
| MATH 664 | Invariant Theory | 3 | 0 | 3 | 7.5 | |
| MATH 665 | Algebraic Coding Theory | 3 | 0 | 3 | 7.5 | |
| MATH 666 | Integral Equations | 3 | 0 | 3 | 7.5 | |
| MATH 667 | Theory of Finite Elements | 3 | 0 | 3 | 7.5 | |
| MATH 668 | Spectral Analysis of Differential Operators | 3 | 0 | 3 | 7.5 | |
| MATH 669 | Applied Homology, Computational Approach | 3 | 0 | 3 | 7.5 | |
| MATH 670 | Set Theoretic Topology | 3 | 0 | 3 | 7.5 | |
| MATH 671 | Fuzzy Optimization | 3 | 0 | 3 | 7.5 | |
| MATH 672 | Algebra | 3 | 0 | 3 | 7.5 | |
| MATH 673 | Computational Commutative Algebra | 3 | 0 | 3 | 7.5 | |
| MATH 674 | Group Theory and Its Applications | 3 | 0 | 3 | 7.5 | |
| MATH 675 | Applications of Modules and Representation Theory | 3 | 0 | 3 | 7.5 | |
| STAT 501 | Theory of Statistics | 3 | 0 | 3 | 7.5 | |
| STAT 502 | Stochastic Processes | 3 | 0 | 3 | 7.5 | |
| STAT 503 | Probability Theory | 3 | 0 | 3 | 7.5 | |
| STAT 504 | Nonparametric Statistics | 3 | 0 | 3 | 7.5 | |
| STAT 505 | Applied Statistical Analysis | 3 | 0 | 3 | 7.5 | |
| STAT 506 | Multivariate Statistics and the Theory of Copulas | 3 | 0 | 3 | 7.5 | |
| STAT 551 | Actuaria | 3 | 0 | 3 | 7.5 | |
| STAT 552 | Ordered Random Variables | 3 | 0 | 3 | 7.5 | |
| STAT 553 | Reliability | 3 | 0 | 3 | 7.5 | |
| STAT 554 | Statistical Process Control | 3 | 0 | 3 | 7.5 | |
| STAT 555 | Risk Analysis | 3 | 0 | 3 | 7.5 | |
| STAT 556 | Linear Statistical Models | 3 | 0 | 3 | 7.5 | |
| STAT 557 | Time Series Analysis | 3 | 0 | 3 | 7.5 | |
| STAT 558 | Design of Experiment | 3 | 0 | 3 | 7.5 | |
| STAT 559 | Advanced Probability Theory | 3 | 0 | 3 | 7.5 | |
| STAT 560 | Statistical Methods in Biology and Medical Sciences | 3 | 0 | 3 | 7.5 | |
| STAT 561 | Statistical Softwares and Simulation | 3 | 0 | 3 | 7.5 | |
| STAT 562 | Combinatorial Analysis and Discrete Distributions | 3 | 0 | 3 | 7.5 | |
| STAT 563 | Statistical Decision Theory | 3 | 0 | 3 | 7.5 |
The students studying in this Ph.D program are required:
Take at least 7 courses with 21 local credits (Master degree holders),
Take at least 14 courses with 42 local credits (Undergraduate degree holders),
Take 240 ECTS and obtain a GPA of 3.00 over 4.00 (Master degree holders),
Take 300 ECTS and obtain a GPA of 3.00 over 4.00 (Undergraduate degree holders),
Pass the proficiency exam,
Succeed in thesis proposal and thesis exam,
Prepare and defend a doctoral thesis,
Score at least CC/S in all the master program courses required by the program, and at least CB/S in all the doctoral program courses.
B-2- FOREIGN NATIONALS:
To hold an Undergraduate or Master Degree with Thesis Diploma (Students, who enrolled to non-thesis master programs before February 06, 2013, are not required to hold “Master Degree with Thesis Diploma”.),
To graduate from undergraduate program (4 year) of Departments of Mathematics, Statistics, Chemistry, Biology, or Physics, Mathematical Engineering, Computer Engineering, Software Engineering, Industrial Engineering, Industrial Systems Engineering, Electrical Electronics Education, Economics, Logistics Management, Finance, or Financial Mathematics to apply for Applied Mathematics or Statistics master program,
To obtain the scores in the exams specified below, or obtain an equivalent score in the internationally recognized foreign language exams that are deemed equivalent to the exams specified below by the Higher Education Board,
|
Type of Exam |
Score |
|
KPDS ( Public Personnel Language Exam ) |
70* |
|
UDS ( Interuniversity Foreign Language Exam ) |
70* |
|
YDS (Foreign Language Placement Exam) |
70 * |
Want to improve your English level for admission?
Prepare for the program requirements with English Online by the British Council.
- ✔️ Flexible study schedule
- ✔️ Experienced teachers
- ✔️ Certificate upon completion
📘 Recommended for students with an IELTS level of 6.0 or below.
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