PhD Computer Science and Engineering
- Entry Requirements
- Masters degree in a STEM field. (STEM = Science, Technology, Engineering & Mathematics)
- Registration: All UCU Study Centers (program offered 70% online)
- Mode of delivery: Blended (70% online)
Overview
The Doctor of Philosophy in Computer Science and Engineering (PhD CSE) programme of Uganda Christian University is a research-oriented degree designed to train individuals whose research output will contribute new knowledge in broader body of computer science and engineering. This PhD CSE is designed to accommodate multidisciplinary scientists with the commitment, and desires to do research related to the three tracks, i.e., Computer Science, Data Engineering, and Mechatronics and Robotics Engineering.
1. Admission Requirement
The admission criteria to the PhD Computer Science shall consist of the following;
- The common regulations for the Doctorate degree as specified in the Uganda Christian University Statute shall be applicable.
- The programme shall be open to holders of Masters degrees in Science, Technology, Engineering and Mathematics (STEM) fields as determined by the by National Council of Higher Education in Uganda; or
- The programme shall be open to holders of any Masters degree with evidence of acquisition of sufficient advanced knowledge in computer science or Engineering by virtue of research research or related work.
- Applicants must demonstrate requisite knowledge (from previous studies, research or work) necessary to successfully undertake the proposed research and must submit a concept note of the proposed research project they wish to undertake. Applicants may udertake and pass masters-level remedial courses offered at the Department of Computing and Technology to fulfill this requirement before they are admitted to the PhD CSE programme.
- In case of international students, their respective country higher education authority should have accredited their programme/ institution. Additionally, their academic documents should be validated by the Uganda National Examinations Board.
- First-class undergraduate degree holders who demonstrate exceptional scholarly and research ability during their respective postgraduate (MSc) programmes after their first year, may apply to upgrade their MSc admission to the PhD CSE provisional admission. Under exceptional circumstances and with recommendation from relevant subject experts at the department, such applicants may apply to have the first two PhD CSE semesters (semesters 1 & semester 2) waived and be allowed to start in the second year of the PhD CSE programme and continue with the rest of the programme.
- Holders of a Master of Philosophy (MPhil) in a related STEM field with evidence of research record equivalent to MPhil CSE graduate requirements may be admitted to the PhD CSE programme in third year to undertake only two years of original and independent research leading to a PhD CSE Dissertation.
- Upon satisfying the admission requirements, the PhD CSE direct applicants shall be provisionally
admitted to the PhD CSE programme. The admission shall remain provisional until the the
candidate:
- Successfully submits, defends and passes a PhD CSE proposal, and
- Successfully attempts and passes semester one and semester two taught courses accumulating a total of thirty (30) credits.
- In case of candidates that are required to attempt and pass remedial courses prescribed by the faculty at the time of provisional admission. All the required courses shall be attempted and passed before the student is fully admitted to the programme.
- Upon obtaining full admission status, the PhD CSE student shall be assigned a PhD advisor who shall work with the student in preparation for a PhD candidacy qualifying seminar. A PhD candidacy qualifying seminar shall be organized for every PhD student who successfully completes the first part of the PhD CSE programme, i.e., four taught semesters, and a conference or journal publication. The seminar shall act an exam for admission to PhD CSE candidacy. Successful candidates shall be cleared to continue with independent research leading to a PhD CSE Dissertation. Unsuccessful candidates may be advised either redo the seminar within six months or awarded a terminal Master of Philosophy in Computer Science and Engineering (MPhil CSE). At this point, successful candidates may apply for change of advisors or decide to continue with the current advisor(s).
- Students admitted through the upgrading from relevant MSc programme and obtained a waiver of the first two semesters to start in second year must submits, defend and successfully pass a PhD
- CSE proposal to be fully admitted.
- Students admitted to third year by virtual of holding a Master of Philosophy (MPhil) submits, defend and successfully pass a PhD CSE proposal to be fully admitted to PhD CSE programme and must be prepared to give a PhD candidacy qualifying seminar within six months of full admission. In the event that the student fails to pass during the PhD CSE qualifying seminar, the student’s admission shall be canceled and advised to reapply through direct entry.
- All PhD CSE candidates are responsible for maintaining their registration status for a maximum of four years. Beyond the maximum four year programme duration, PhD CSE candidates may be allowed two concessional years on the programme to complete their research. After the two concessional years, the candidate shall be automatically deregistered unless extra time is granted by the Faculty Board which may not exceed two extra years. The application to extend the study period shall be made by the candidate before the expiry of the concessional years. Once derigestered, the candidate must apply to the Faculty Board for readmission to the programme if the eight maximum years on the programme have not elapsed. Once the eight years have elapsed and under exceptional circumstances, the candidate may apply to the Deputy Vice Chancellor Academics Affairs through the Faculty board for extra time to complete the PhD CSE research.
As part of the PhD CSE programme, students shall present atleast two Graduate Student Colloquia. The presentations shall be evaluated by the audience and the student will be informed of the results of those evaluations. The colloquia shall be given during the second year of study (during semesters three and four). At least one colloquium may be a review of an article (not authored by the student) in a peer-reviewed journal and the other may be a summary of the student’s ongoing research work in preparation of the PhD candidacy qualifying seminar given at the end of the second year.
2. Conduct of the Programme
2.1 Nature of the Programme
The PhD CSE programme is divided into two parts, the first part has four taught and research courses in total and the second part consists of original scientific research with a PhD Dissertation as the final output.
The taught courses shall be conducted using a blended ODeL approach. Blend of online and face-to-face sessions will be used to facilitate discussions leading to writing technical articles that may be submitted to conferences and peer-reviewed journals.
The first part consists of four courses that offer participants guided study in key areas of computer science and engineering and accumulating forty-five (45) credits. By the end of part one, students are expected to have gained in-depth understanding of existing research, trends and methods in the chosen area of research. As a final output from Part one, students shall be expected to publish a review paper in an accredited journal or two conference papers.
Upon satisfying the requirements for part one of PhD CSE (successfully completing the two-year coursework and publication requirement) the candidates shall be eligible for the award of a Master of Philosophy in Computer Science and Engineering (MPhil CSE). The MPhil CSE shall be awarded under one of the following circumstances.
- The PhD candidate may apply to terminate their continuation on the PhD programme and be awarded with MPhil CSE. Such candidates may reapply for readmission onto the PhD CSE programme to complete Part two of the programme (the research dissertation).
- The PhD candidate shall automatically be awarded with MPhil CSE once their period of study on the PhD CSE programme expires. Such candidates shall not be eligible for readmission onto the PhD CSE programme.
- The PhD candidate shall automatically be awarded with a terminal MPhil CSE if they fail a PhD Dissertation examination twice. Such candidates shall not be eligible for readmission onto the PhD CSE programme.
The second part consists of independent research by the PhD candidate. During this part, PhD candidates with fully approved proposals shall carry out original research under the guidance of a senior researcher nominated by the Faculty.
2.2. Research tracks
The After the first year on the programme, PhD CSE students shall choose a research specialisation track from the five tracks, i.e., Theoretical Computer Science, Applied Computer Science, Data Engineering, Mechatronics & Robotics Engineering, and Computational Transportation Engineering.
The Computer science discipline is the study of computation, automation, and information. Computer science research spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to practical disciplines (including the design and implementation of hardware and software)
Theoretical computer science (TCS) research field comprises two sub-fields: the theory of algorithms, which involves the design and analysis of computational procedures; and complexity theory, which involves efforts to prove that no efficient algorithms exist in certain cases, and which investigates the
classification system for computational tasks. Time, memory, randomness and parallelism are typical measures of computational effort.
Theoretical computer science is a natural bridge between mathematics and computer science, and both fields have benefited from the connection. PhD fellow on this research track may work on exciting problems such as the seven Clay Millennium Problems, e.g., P vs NP Problem, Riemann Hypothesis, Poincar´e Conjecture, Hodge Conjecture, etc..
In this track, research fellows investigate technical (’applied’) aspects of computer science to solve society problems.
Students who offers this track may do research related to;
-
- Computer Architecture & Engineering (ARC): Research in ARC will aim to address next generation issues in computer architecture to address the grand challenge of parallel computation, bringing in techniques from other fields (e.g. machine learning for high impact optimizations), ideas for architecture based on novel substrates, power budget issues, micro-architectural circuit level issues, and architectural issues in the development of sensor networks.
- Biosystems & Computational Biology (BIO): Modern biology is increasingly reliant on the algorithmic and conceptual tools of computer science and electrical engineering. A major factor is the unprecedented growth in the size and scope of biological data sets, including multi-species genomic data, databases of polymorphic variants, databases of protein structure and RNA structure, gene expression data, biochemical measurements from large-scale gene knockout experiments and biomedical data. Representing, manipulating and integrating such data requires an appreciation of ideas from diverse areas such as databases, algorithms, artificial intelligence, graphics, signal processing and image processing. Reasoning about the underlying phenomena that give rise to such data require the systems-level thinking that is the underpinning of areas such as control theory, information theory and statistical machine learning. Ideas from circuit design and nanotechnology play key roles in the design of new biological sensors and actuators. Fellows who opt for this research area will work on biological problems with cross-displinary teams in computing, electronics and biology, and will play key roles in collaborative research projects involving the school of medicine staff and students in collaboration with the UCU Center for Computational Biology and the African Center of Excellence in Bioinformatics.
- Human-Computer Interaction (HCI): Human-Computer Interaction research studies interaction in current and future computing environments, spanning workplaces, homes, public spaces, and beyond. Fellows doing HCI research may conduct research relating to (1) Context-aware computing, i.e., Activity analysis, Embodied and Wearable Computing, Smart Spaces, Location-aware systems, Privacy technologies, Affective Computing; (2) Perceptual Interfaces, i.e., Virtual reality (VR) and Augmented reality (AR), Vision-based interfaces, Conversational interfaces; (3) Collaboration and Learning, i.e., Tutorial and instruction systems, Crowdsourcing, Pattern-based authoring tools, Learning at scale, Remote group collaboration technologies, Citizen science; (4) Digital Design and Fabrication, i.e., Prototyping tools, Computational Design, Creativity-support tools, Sensing technologies; (5) Human-Centered Artificial Intelligence, i.e., Human-robot interaction, Explainable AI, Interactive Machine Learning, Responsible AI, Multimedia retrieval and understanding, Recommender Systems; (6) Interactive Data Exploration and Presentation, i.e., Visualization and visual analytics, Sketch-based and direct manipulation interfaces, Computational notebooks; (7) Optometry and Human Vision Simulation, i.e., Computer aided cornea modeling and visualization, Medical imaging, Virtual environments for surgical simulation, Vision realistic rendering.
- Operating Systems & Networking (OSNT): Fellows doing OSNT research may conduct research relating to (1) Internet architecture, i.e., Overlay architectures. Distributed hashing. Naming. Next generation network design. Peer to peer networking. Mobile and ad-hoc networking. Troubleshooting. Implications for energy efficiency. (2) Security, i.e., Malware detection. Secure routing. Testbeds for security applications. Operating systems security. Intrusion detection/prevention. Availability. Authentication. Botnets and worms (3) Distributed Systems, i.e., Experimental testbeds. Distributed logging. Declarative networking. (4) Operating systems, i.e., OS for sensor networks. Monitoring OS behavior for malware detection. Performance analysis. Programming
- Scientific Computing and Numerical Methods: Fellows opting to do research in this field, may develop scientific software (and hardware standards) backed up with strong theoretical foundations. The objective is to produce industrial-strength software and algorithms and supporting full-scale scientific applications in large interdisciplinary collaborations among scientists, engineers, mathematicians, and computer scientists. The research encompasses symbolic, numerical, and geometric computation, often on parallel or distributed systems. Research may be conducted in (1) Automatic Performance Tuning, i.e., Automatic generation of optimized implementations of computational and communication kernels, tuned for particular architectures and work loads. (2) Mesh generation, i.e., Automatic generation of triangulated meshes to represent physical and computational domains. (3) Matrix computations, i.e., Numerical algorithms and software for fast and accurate numerical linear algebra. (4) Floating point, i.e., Extended precision arithmetic. Reliable floating point standards. Architectural and run time implications of floating point standards. Programming language implications of floating point standards.
- Quantum Computing – Quantum algorithms (e.g., Shor’s, Grover’s), Quantum hardware and simulation, Quantum-safe cryptography.
- Informatics: Informatics is the study of the structure, behaviour, and interactions of natural and engineered computational systems. Informatics studies the representation, processing, and communication of information in natural and engineered systems. It has computational, cognitive and social aspects.
Artificial Intelligence (AI) and Machine Learning (ML) Engineering are vast and evolving fields with a wide array of research concentrations.
Students who choose this research track may undertake research related to:
- Natural Language Processing (NLP):
- Computer Vision
- Speech and Audio Processing.
- Robotics and Autonomous Systems
Software engineering is a discipline that focuses on designing, developing, testing, and maintaining software applications. It combines computer science and engineering principles to create reliable, efficient, and user-friendly software solutions Students who choose this research track may undertake research
related to:
- Software Development Methodologies – Agile, DevOps, Lean, and hybrid methods; Continuous integration and continuous deployment (CI/CD); Toolchain integration and automation
- Software Architecture and Design – Microservices, serverless, and event-driven architectures; Modeldriven engineering (MDE); Design patterns and anti-patterns
- Software Testing and Quality Assurance – Automated testing and test generation; Regression testing and test suite prioritization; Mutation testing and fuzzing; Software reliability and defect prediction
- Software Testing and Quality Assurance – Automated testing and test generation; Regression testing and test suite prioritization; Mutation testing and fuzzing; Software reliability and defect prediction
- Formal Methods and Verification – Model checking and theorem proving; Program analysis and static analysis; Correctness-by-construction
- Software Maintenance and Evolution – Refactoring and technical debt management; Reverse engineering; Code smells and anti-pattern detection
- Software Security and Privacy – Secure software development lifecycle (SSDLC); Vulnerability detection and mitigation; Privacy-preserving software engineering
- AI/ML in Software Engineering (AI4SE & SE4AI) – Code generation and synthesis (e.g., GitHub Copilot); Bug prediction using machine learning; AI-assisted testing and debugging
- Human Aspects of Software Engineering – Developer productivity and burnout; Collaborative development (e.g., open-source dynamics); UX and usability in developer tools
- Requirements Engineering; Natural language processing for requirements extraction; Ambiguity detection; Goal modeling and prioritization
- Empirical Software Engineering – Mining software repositories (e.g., GitHub, Stack Overflow); Controlled experiments and case studies; Metrics and measurement
- Software Sustainability and Green Computing Energy-efficient software design; Carbon-aware cloud services; Lifecycle sustainability in development
- Cloud and Edge Computing – Deployment automation; Scalability and resilience in distributed systems; Container orchestration (Kubernetes, Docker)
- Software for Emerging Technologies, i.e., Quantum software engineering; Blockchain-based applications; Engineering for IoT, AR/VR, and robotics
Security Engineering focuses on designing and building systems that remain secure under threat. Security Administration focuses on deploying, managing, and maintaining secure IT infrastructure.
Students who choose this research track may undertake research related to:
- Security Engineering (1) Cryptography, i.e., Research in cryptography spans from theory to applications, including significant research efforts in complexity-theoretic approaches to cryptography, development of new cryptographic systems, cryptanalysis, protocol development, applied cryptography, quantum computation, and applications that include electronic commerce, electronic voting, wireless communications, and protocols for sensor webs. (2) Privacy, i.e., privacy in wireless sensor webs, privacy in RFID systems, privacy issues in databases, privacy in web based applications. (3) Social implications of security, (4) Sensor web security, i.e., development of the sensor web model – based around wireless ”motes” with limited computing power and sensing devices. (5) Testbeds for security, i.e., large-scale testbeds for different ideas including: * DETER – a virtual Internet for testing prorogation of worms and attacks etc.. (6) Security, programming languages, and software engineering, i.e., the interaction between programming languages and computer security – an area often called ”software security.” (7) Human interfaces and security, i.e., Human-centric security; (8) Identity and integrity, i.e., Preventing ”phishing” and attacks (9) Network security, i.e., High-performance network security monitoring and intrusion prevention. (10) Electronic voting, i.e., security of electronic voting, studies voting technology and its policy implications; performing top-to-bottom evaluation of voting systems. (11) Beyond Technical Security, i.e., understanding the factors that drive threats to security, tackle key social and economic elements of security: how the motivations and interactions of attackers, defenders, and users shape the threats we face,how they evolve over time, and how they can best be addressed. (12) Secure Software Development Lifecycle (SSDLC)- Integrating security into Agile and DevOps; Threat modeling during the design phase.(13) Vulnerability Detection and Prevention – Static and dynamic code analysis; Memory safety (buffer overflows, use-after-free); AI-based vulnerability detection (e.g., deep learning models)(14) Secure Coding Practices – Language-based security (e.g., Rust for memory safety); Code obfuscation and secure compilation (15) Authentication and Authorisation – Multi-factor authentication (MFA); Role-Based and Attribute-Based Access Control (RBAC/ABAC); Biometric and behavioral authentication (16) Cryptography Engineering -Implementation of encryption algorithms; Key management systems; Post-quantum cryptography (17) Formal Verification of Security Properties – Proving software satisfies confidentiality/integrity; Model checking, theorem proving (18)Security in AI/ML Systems – Adversarial attacks and defenses; Secure model training and inference
- Security Administration (1) Identity and Access Management (IAM)- Federated identity (SSO, OAuth2, SAML); Zero Trust Architecture; (2) Security Information and Event Management (SIEM)- Log analysis and threat detection; Anomaly detection using machine learning; (3) Incident Response and Forensics – Automated incident response; Digital evidence collection and chain of custody; (4) Patch Management and Configuration Control – Vulnerability lifecycle management; Secure configuration baselines; (5) Cloud Security Administration – Securing multi-tenant environments; Policy enforcement and auditability; (6) Compliance and Governance – GDPR, HIPAA, PCI-DSS, ISO 27001; Risk assessment frameworks (NIST, FAIR); (7) Network and Endpoint Security – Firewalls, IDS/IPS, anti-malware; EDR XDR (Endpoint/Extended Detection and Response)
Network engineering is a specialized field within computer science and engineering that focuses on the design, implementation, and management of computer networks. Students who choose this research track may undertake research related to:
- Computer Networks Architecture & Protocols – TCP/IP performance tuning; Congestion control and QoS (Quality of Service); Routing protocols (e.g., OSPF, BGP, EIGRP)
- Wireless & Mobile Networks- 4G/5G/6G technologies; Wi-Fi optimization; Mobile ad hoc networks (MANETs) and vehicular networks (VANETs)
- Network Security – Intrusion Detection/Prevention Systems (IDS/IPS); Secure routing and data transmission; Firewalls, VPNs, and access control mechanisms
- Software-Defined Networking (SDN) – Centralized network control; Dynamic traffic engineering; OpenFlow and programmable data planes
- Network Function Virtualization (NFV) – Replacing hardware appliances with virtual functions; Service chaining and orchestration; Scalability and performance issues
- Cloud, Edge, and Fog Networking – Low-latency services at the edge; Resource offloading strategies; Data routing in fog computing
- Internet of Things (IoT) Networking – LPWAN protocols (e.g., LoRa, NB-IoT); Energy-efficient communication; Scalability and data aggregation
Automation and Control Engineering research is a field that sits at the heart of modern innovations in smart systems, robotics, industrial automation, AI, and sustainable technologies. Students who choose this research track may undertake research related to:
- (1) Control Theory – Linear, Nonlinear, Optimal,Adaptive, Robust, and Stochastic control; (2) Modeling and Simulation of Dynamic Systems – Mathematical modeling (ODEs, PDEs, transfer functions, state space), System identification, Real-time simulation tools (MATLAB/Simulink, Modelica) (3) Industrial Automation & Process Control – PLCs, SCADA, DCS systems; Batch and continuous process control; Fault detection, diagnostics, and fault-tolerant control; (4) Instrumentation and Sensors – Industrial sensors and data acquisition, Signal conditioning and filtering, Smart sensor networks.
- (5) Autonomous Systems & Robotics – Path planning and motion control; Multi-agent systems and swarm robotics; Sensor fusion (IMU, LiDAR, vision); Autonomous vehicles and drones (6) Embedded and Real-Time Control Systems -Microcontroller/FPGA-based control; Real-time operating systems (RTOS); Safety-critical systems (e.g., automotive, aerospace); (7) Networked Control Systems – Control over communication networks (IoT, WSAN); Time-delay and packet loss handling; Event-triggered and self-triggered control
- (8) Machine Learning in Control Systems – Reinforcement Learning (RL) for optimal control; Deep learning for predictive control; Hybrid data-driven and model-based control (9) Digital Twin and Smart Systems – Real-time mirrored systems for predictive maintenance; Twin-driven optimization in manufacturing and energy systems (10) Human-in-the-Loop (HIL) Control – Brain-computer interfaces in control; Telerobotics and remote manipulation; (11) Haptic and Force Feedback Systems- Control for virtual and augmented reality; Haptics in surgery and rehabilitation robotics (12) Edge and Cloud-Controlled Systems – Offloading control logic to edge/cloud, Latency-aware and distributed control.
Intelligent Transportation Systems (ITS) Engineering is a dynamic and interdisciplinary research field that integrates engineering, Artificial Intelligence, data science, and urban planning to create safer, smarter, and more efficient transport solutions.
Students who offer this track may do research related to;
(1) Traffic Management & Optimization – Real-time traffic monitoring and prediction; Intelligent traffic signal control systems; Adaptive traffic light scheduling; Congestion detection and mitigation; (2) Connected
and Autonomous Vehicles (CAVs) – Vehicle-to-Everything (V2X) communication; Sensor fusion for autonomous driving (LiDAR, radar, camera); Path planning and collision avoidance; Cooperative driving (platooning, lane merging); (3) Intelligent Navigation and Route Planning – Dynamic routing using real-time data (weather, traffic, events); AI-based route optimization (shortest time vs. energy
efficiency); Integration with public transport systems; Multi-modal transport solutions; (4) Cybersecurity & Data Privacy in ITS – Secure communication between vehicles and infrastructure (V2V, V2I); Privacy-preserving data sharing; Threat modeling and intrusion detection systems; Blockchain for decentralized
trust in vehicle networks (5) Artificial Intelligence in Transportation – Machine learning for traffic flow prediction; Computer vision for vehicle/pedestrian detection; AI for predictive maintenance of transportation infrastructure; AI in behavior modeling and driver assistance (6) Sustainable and Green Transportation – Electric vehicle (EV) infrastructure and charging optimization; Energy-efficient route planning; Eco-driving recommendation systems; Smart public transport systems (green buses, smart metros) (7) Smart Infrastructure & IoT Integration – Smart traffic lights and road signs; Integration of IoT sensors into roads, signals, and vehicles; Real-time condition monitoring of roads and bridges; Digital twins of transportation networks (8) Human Factors & Safety – Driver fatigue and distraction detection; Pedestrian behavior modeling; User-centric interface design for in-vehicle systems; Enhanced situational awareness for drivers and passengers; (9) Big Data & Mobility Analytics – Analyzing GPS, social, and sensor data for mobility insights; Origin-destination modeling and demand prediction; Spatiotemporal analytics for traffic trends; AI-driven transportation planning (10) Policy, Economics, and Ethics in ITS – Cost-benefit analysis of ITS deployments; Ethical challenges in autonomous decision-making; Policy frameworks for smart mobility; Urban mobility equity and accessibility.
3. Course Curriculum
PART I (Year 1 & Year 2)
| Code | Module Name | Type | CU |
|---|---|---|---|
| (i) Semester One (3 courses) | |||
| CSC9101 | Philosophy of Computer Science | C | 5 |
| CSC9102 | Advanced Algorithm Analysis and Design | C | 5 |
| MEC9101 | Modelling Complex Systems | C | 5 |
| (ii) Semester Two (3 courses) | |||
| TST9131 | Advanced Christian Ethics and worldviews | C | 5 |
| RSM9103 | Research Methods and Publications | C | 5 |
| ICT9102 | Technical Writing and Integrated Technical Communication | C | 5 |
| (iii) Semester Three (2 courses) Students choose one track | |||
| CSE9301 | CSE Graduate Colloquia I | C | 5 |
| Research tracks (Select one course from any track in the tracks table) | |||
| CSC9201 | Theory of Computational Complexity | C | 5 |
| CSC9202 | Special Topics in Computer Science | A | 5 |
| (iv) Semester Four (3 courses) | |||
| CSE9302 | CSE Graduate Colloquia II | C | 5 |
| PSY9301 | Philosophy of Methods | A | 5 |
| ICT9103 | Scientific Data and Workflow Management | A | 5 |
PART II (Year 3 & Year 4)
| Code | Module Name | Type | CU |
|---|---|---|---|
| Core Modules | |||
| CSE9401 | PhD CSE Dissertation | C | 60 |
Research track specific courses – PhD CSE
| Code | Module Name | Type | CU |
|---|---|---|---|
| Core Modules | |||
| Research specialization tracks (Select one course from any track) | |||
| Research track 1 (Computer Science – Theoretical/Applied) | |||
| CSC9201 | Theory of Computational Complexity | C | 5 |
| CSC9202 | Special Topics in Computer Science | A | – |
| Research track 2 (Artificial Intelligence & ML Engineering) | |||
| DSC9201 | Advanced AI Models, evaluation and selection | C | 5 |
| DSC9202 | Special Topics in AI & ML Engineering | A | – |
| Research track 3 (Software Engineering) | |||
| MEC9201 | Advanced Software Engineering Principles | C | 5 |
| MEC9202 | Special Topics in Software Engineering | A | – |
| Research track 4 (Automation, Control and Robotics Engineering) | |||
| MEC9201 | Advanced Control Systems Design (Sensors, Controllers and Actuators) | C | 5 |
| MEC9202 | Special Topics in Mechatronics and Robotics | A | – |
| Research track 5 (Computational Transportation Engineering) | |||
| CTE9201 | Sustainable Digital Transportation and Connected Vehicle Infrastructure | C | 5 |
| CTE9202 | Special Topics in Computational Transportation Engineering | A | – |
How to Apply?
You can apply either online (recommended) or download and fill out application forms and submit them physically at any of our campuses in Kampala, Mukono, Kabale, Mbale or Arua.