PhD program
Computer Science

Computer Science

QUALIFICATION

  • Scientific and pedagogical direction - Doctor of Philosophy (PhD)

MODEL OF GRADUATING STUDENT

ON1 Interpreting fundamental concepts in computer science and new programming paradigms, applying them in software design and development.
ON2 Formulating scientific goals, plan research and conducting large-scale computational experiments in specific applications.
ON3 Critically analyzing, evaluating and synthesizing new and complex ideas in the field of computer science.
ON4 Applying big data processing and data mining methods to solve resource-intensive tasks.
ON5 Developing computational algorithms for engineering tasks and implementing them in high-performance systems.
ON6 Exploring computational complexity and stability of algorithms.
ON7 Analyzing and evaluating the reliability and fault tolerance of computer systems.
ON8 Comparing, analyzing and interpreting complex experimental data and draw conclusions.
ON9 Presenting cutting-edge topics and research results at international and national conferences, seminars and workshops both in front of specialists and in an audience that does not have relevant professional training.
ON10 Contributing to the original research that expands the boundaries of knowledge by developing a significant amount of work, publish the research results in the form of scientific articles in Kazakhstan and foreign publications.
ON11 Compiling explanatory notes and applications for research projects, carry out planning, as well as guide and manage research in computer science and related interdisciplinary areas.
ON12 Organizing research, design, educational and professional activities, participating in scientific, state and industrial research as part of a team, being prepared for correct and tolerant interaction in society, for social interaction and cooperation to solve scientific and technical problems.

Program passport

Speciality Name
Computer Science
Speciality Code
8D06103
Faculty
Information technology

disciplines

Academic Writing
  • Number of credits - 2
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of discipline: to develop professional competencies of PhD students for their research projects and publications while following legislative norms. Doctoral students will learn to formulate research questions, navigate literature, state study provisions, prepare patent and copyright documents, conduct discussions, and write project reviews. This course covers aspects of research formulation, interdisciplinary studies, and describing research work in different languages.

Advanced algorithms and complexity
  • Number of credits - 5
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline teaches analyzing advanced algorithms and computational complexity. PhD students will learn to define tasks, evaluate resource models, apply algorithmic and proof methods, recognize evidence flaws, and correlate algorithms with complexity indicators. Topics include greedy algorithms, dynamic programming, network flows, NP-completeness, computability, Turing machines, approximation algorithms, parallel computations.

PhD thesis writing and defence
  • Number of credits - 12
  • Type of control - Докторская диссертация
  • Description - The main purpose of "PhD thesis writing and defence": of a doctoral dissertation is the formation of the doctoral students' ability to disclose the content of research work for the defense of the thesis. During the study of course, doctoral student's should be competent in: 1. to substantiate the content of new scientifically grounded theoretical and experimental results that allow to solve a theoretical or applied problem or are a major achievement in the development of specific scientific directions; 2. explain the assessment of the completeness of the solutions to the tasks assigned, according to the specifics of the professional sphere of activity; 3. they can analyze alternative solutions for solving research and practical problems and assess the prospects for implementing these options; 4. apply the skills of writing scientific texts and presenting them in the form of scientific publications and presentations. 5. to plan and structure the scientific search, clearly highlight the research problem, develop a plan / program and methods for its study, formalize, in accordance with the requirements of the State Educational Establishment, the scientific and qualification work in the form of a thesis for a scientific degree Doctor of Doctor of Philosophy (PhD) on specialty «8D07502 – Standardization and certification (by industry)». During the study of the discipline doctoral student will learn following aspects: Registration of documents for presentation of the thesis for defense. Information card of the dissertation and registration-registration card (in the format Visio 2003). Extract from the minutes of the meeting of the institution, in which the preliminary defense of the thesis took place. Cover letter to the Higher Attestation Commission. Expert conclusion on the possibility of publishing the author's abstract. Expert opinion on the possibility of publishing a dissertation. Minutes of the meeting of the counting commission. Bulletin for voting. A shorthand record of the meeting of the dissertational council. List of scientific papers. Response of the official opponent. A review of the leading organization. The recall of the scientific adviser.

Research and analysis of algorithms
  • Number of credits - 5
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to teach applying tools and methods for algorithmic solutions with strict theoretical time and space limits. PhD students will compare and apply key data structures, analyze algorithms' behavior, compare temporal complexity, describe algorithms functionally and procedurally, and develop and apply fundamental algorithms to solve real problems.

Scientific Research Methods
  • Number of credits - 3
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The purpose of this discipline is to master the basics of the methodology of scientific research, consideration of different levels of scientific knowledge. Study of the stages of conducting research, including the selection of the direction of research, staging of scientific and technical problems, conducting theoretical and experimental research, recommendations for formalization of the formulation The course is also aimed at reviewing the basics of inventive work, patent search and sample plan for a PhD dissertation

Data for 2021-2024 years

disciplines

Advanced machine learning
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to teach optimizing, deploying, and scaling advanced machine learning models. PhD students will learn to analyze samples, perform probabilistic modeling, develop optimization algorithms, create machine learning solutions, implement and evaluate reinforcement learning models, and develop recommendation systems.

Big data Analytics
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to develop skills in evaluating big data analysis technologies and creating software products. Doctoral students will characterize and compare data sets, address challenges like high dimensionality and scalability, integrate machine learning libraries with modern technologies, optimize model parameters, and build applications using neural networks and the TensorFlow framework.

Computational algorithms of engineering problems of hydrodynamics on high-performance systems
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to develop skills in solving engineering problems using computational algorithms on high-performance systems, focusing on the Navier-Stokes and Euler equations. PhD students will describe partial differential equations, analyze computational methods' properties, solve equations computationally, parallelize hydrodynamics problems, and develop software for engineering applications.

Deep learning
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to develop skills in utilizing and creating deep neural networks for analyzing big data. Doctoral students will describe principles and applications of deep learning, apply key concepts in training and modeling, integrate course content into research, implement neural networks using software libraries, and critically assess current research in neural networks and applications.

High Performance Computing Models
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline develops the ability to manage parallel computing technology on multiprocessor systems with distributed or shared memory. Doctoral students will learn to represent multiprocessor structures, analyze and decompose computational schemes, evaluate data transfer complexities, simulate parallel programs, and create computing system models. Topics include data transfer mechanisms, topology representation, parallel algorithm development, and shared memory systems.

High-performance programming with multi-core and graphics processors
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to develop expertise in optimizing program performance by understanding computing platforms. PhD students will analyze modern high-performance processors, utilize extended command sets, program complex systems, develop software, and evaluate programming methods for multi-core and graphics processors, including applications like matrix operations and fast Fourier transform.

Information resource modeling
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to teach modeling information resources using structural and system analysis. PhD students will develop and research information resource models, analyze and optimize information systems, conduct experiments, analyze results, prepare scientific publications, and predict technology trends. Topics include mathematical modeling, system analysis, simulation, and research methods in information processes.

Numerical methods for scientific computing tasks
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - The discipline aims to teach constructing algorithms and analyzing methods for solving nonlinear differential equations. PhD students will learn to describe numerical analysis approaches, develop computational algorithms, ensure solution accuracy, solve optimal control problems, and perform efficient computations. Topics include finite-difference schemes, Navier-Stokes equations, turbulent flow modeling, and numerical methods for boundary layer processes.

Reliability in distributed systems
  • Type of control - [RK1+MT+RK2+Exam] (100)
  • Description - This discipline aims to develop skills in creating reliable algorithmic, technical, and software solutions for distributed computing systems. PhD students will master methods to assess system reliability, perform reliability calculations, develop effective models, analyze factors influencing reliability, and implement tools for creating efficient software packages.

Data for 2021-2024 years

INTERNSHIPS

Pedagogical
  • Type of control - Защита практики
  • Description - Formation of practical, educational-methodical skills of conducting lectures, seminars, creatively apply scientific, theoretical knowledge, practical skills in teaching activities, conduct training sessions in the disciplines of the specialty; own modern professional techniques, methods of training, use in practice the latest theoretical, methodological advances, make educational, methodological documentation.

Research
  • Type of control - Защита практики
  • Description - The purpose of the practice: gaining experience in the study of an actual scientific problem, expand the professional knowledge gained in the learning process, and developing practical skills for conducting independent scientific work. The practice is aimed at developing the skills of research, analysis and application of economic knowledge.

Data for 2021-2024 years