About the Program
The M.Sc. in Software Engineering at Braude College is designed for graduates seeking to deepen their knowledge and advance their skills in software development, management, and applied engineering research. The program provides comprehensive expertise in applied and scientific software engineering, data mining, and learning engineering systems, mainly preparing students for senior technical roles in industry, project leadership positions, and possible research careers.
The studies serve as a career accelerator, helping graduates advance professionally or secure positions in prominent companies. The flexible schedule accommodates working professionals. The curriculum is regularly updated to reflect global developments in software engineering and machine learning.
Program Goals & Learning Outcomes
By completing the M.Sc. program, students will:
- Acquire advanced theoretical and practical knowledge in machine learning systems, software engineering, including software development, systems design, quality assurance, and project management
- Develop skills in leading and managing large-scale software projects, including planning, resource management, team leadership, and project execution
- Gain the ability to tackle complex engineering problems using research-based methods, bridging academic research with real-world software development needs
- Master advanced topics, including algorithms, system architectures, intelligent and learning systems, numerical analysis, software verification and validation, and specialized areas based on elective choices
Graduates are qualified for roles as senior software engineers, team leaders, project managers, R&D engineers, or may pursue further doctoral research.
Program Structure & Curriculum
Duration: The standard program length is two years, with the option to extend to three years for working professionals or those preferring a lighter course load.
Credit Requirements: Students must complete at least 39 academic credits:
- 21 credits from compulsory core courses (typically 5–6 courses)
- 6 credits for the master’s thesis (final project, usually spanning two semesters)
- 11–12 credits from elective courses
Core Courses
Compulsory courses typically include:
- Advanced Software Development Methodologies
- Software Quality Assurance and Process Improvement
- Software Project Management
- Machine Learning Systems
- Algorithm analysis and development
- Data security
- Principles of Programming Using Cloud Technologies
- Final Project
Elective Courses
Students may choose from offerings such as:
- Numerical Methods & Analysis
- Advanced Algorithms/Approximation Algorithms
- Seminar on Databases/Data Management Systems
- Usability/ Human-Computer Interaction
• Computational Biology & Bioinformatics
• Generative AI models in Machine learning
Note: Elective availability may vary by academic year.
Final Project
The final project enables students to apply their knowledge by analyzing a complex software problem (potentially for a real organization), designing and implementing a solution, testing it, documenting the process, and presenting results. Projects may be completed individually or in teams under faculty supervision.
Research Focus: Deep Learning, Algorithms, and Engineering Applications
Research and innovation are central to the program. Students engage in studies and the final project process with modern research directions in deep learning, machine learning, signal and data analysis, and intelligent software systems.
Key research areas include:
- Computer vision, pattern recognition, and multimedia signal processing
- Generative models, including GANs and diffusion models
- Machine learning for robotics, automation, and real-time systems
- Numerical and optimization methods for large-scale learning architectures
- Intelligent decision-support systems and AI-driven software engineering
- Applied data science for industrial and engineering processes
- Advanced program complex development
- Program testing and quality assurance
- System architecture design
Students may join research groups or develop final projects in collaboration with industry partners, gaining exposure to real-world applications and advanced computational methods.
Faculty Research Expertise
Prof. Zeev Volkovich (Head of the Program) conducts research in machine learning, deep learning algorithms, data mining, and high-dimensional data analysis. His work includes developing innovative methods for clustering, anomaly detection, similarity measurement, nonlinear modeling, and the analysis of complex networks and dynamic systems.
Dr. Renata Avros specializes in machine learning, computational linguistics, authorship analysis, and statistical text modeling. Her research explores stylistic analytics, feature engineering, and classification methods for language-based data, combining AI techniques with interdisciplinary applications.
Dr. Zaharya Frankel works in software engineering methodologies, quality assurance, and bioinformatics. His research seeks to improve the reliability and robustness of complex software systems in these areas.
Dr. Katerina Korenblat specializes in computational and formal methods, the quantitative modeling and simulation of distributed systems, formal verification and system specification, alongside the application of machine learning techniques to analyze complex datasets.
Dr. Dvora Toledano focuses on speech and audio signal processing, machine learning for multimedia systems, and pattern analysis. She develops algorithms for speech recognition, acoustic feature extraction, and intelligent interpretation of audio data.
The program’s faculty members have contributed over 100 publications to the scientific literature.
Admission & Eligibility
Eligibility is open to candidates demonstrating academic excellence who hold a Bachelor’s or a Master’s degree in Software Engineering, Computer Science, Information Systems, or allied engineering and scientific disciplines (e.g., Electronics, Mechanical Engineering, Mathematics, or Physics).
The flexible scheduling (two- or three-year options) makes the program suitable for working professionals, those with other commitments, and students who prefer a moderate pace.
Who Should Apply
This M.Sc. is suitable for:
- Software engineers seeking advanced academic and professional development
- Professionals working in software or high-tech who wish to move into senior or managerial roles
- Individuals interested in bridging academic research with practical industry applications
- Engineers aiming to specialize in AI, computational systems, and data-driven engineering
- Those planning to continue toward doctoral studies
- Anyone seeking a flexible master’s program that accommodates work and other responsibilities
Key Facts — M.Sc. in Software Engineering at Braude College
- Up-to-date and industry-relevant curriculum: An advanced Master’s program in Software Engineering designed to keep pace with global developments in software research and industry practice
- Strong professional prospects: Graduates are equipped to lead and complete complex software and AI projects with quality and efficiency; many alumni find roles in high-tech companies, research labs, and R&D teams in Israel and abroad
- Research-oriented faculty: The program combines scientific research foundations with applied software engineering. Faculty members publish in leading journals and integrate advanced methods into teaching, ensuring students are exposed to cutting-edge approaches
- Industry connections: The program maintains relationships with technology companies and academic partners that help bridge academic learning and professional application. Students benefit from opportunities for knowledge exchange and collaboration
- Practical project experience: Students gain significant hands-on experience through advanced coursework and a comprehensive final project that addresses real engineering problems, including exposure to contemporary software methods and complex algorithms
• Real-world professional readiness: The program prepares students to complete a major independent project (master’s thesis) focused on solving a practical engineering problem, often with direct relevance to industrial or research settings - Flexible study format: The M.Sc. program is structured over two years with flexible scheduling (including afternoons and Friday classes) to support students who work alongside their studies