Facilities
We have specialist labs and industry-standard softwareReal-world learning with industry-standard tools
Students have access to powerful high-performance computing labs, equipped with:
- Intel® Core i7 and i9 machines
- 16GB–32GB RAM
- NVIDIA GPUs based on the Ampere architecture
These systems are optimised for deep learning, VR rendering, large-scale data analysis, and simulation projects.
Students also work hands-on with Raspberry Pi devices, particularly in modules like Operating Systems and Embedded Systems. Past student projects include building:
- Firewalls
- Wi-Fi routers
- Jukebox systems
- Digital radios
You'll work with multiple operating systems throughout the curriculum — including Windows, Ubuntu, Raspbian OS, and Kali Linux — gaining practical experience across platforms widely used in both industry and research.
To support flexible learning, we offer AppsAnywhere – a cloud-based platform that gives students remote and on-campus access to specialist software.
We also offer specialist facilities relating to our core areas of study:
1. Artificial Intelligence (AI)
Develop foundational and advanced AI skills, with a strong emphasis on Python programming. Modules include:
- Deep Learning – Understand modern AI models such as CNNs, RNNs, and RBMs
- Computer Vision – Use OpenCV for image augmentation, recognition, and medical analysis
- Natural Language Processing (NLP) – Build chatbot frameworks and analyse language data
- Python for AI & Visualisation – Learn core Python programming, visualisation, and model-building
2. Data Science & Engineering
Master tools and techniques used by data professionals to gather, analyse, and visualise information.
- Data Mining – Work with SAS, Enterprise Miner, and Enterprise Guide to identify patterns and build predictive models
- Data Management & Visualisation – Gain hands-on experience with SSIS, SSMS, Tableau, and Power BI
- Smart Internet & Cloud Platforms – Explore Industry 4.0, AWS, Red Hat OpenShift, and DevOps pipelines
- Virtualisation – Deploy and manage environments with VirtualBox
- Statistical Analysis – Use R, Python (Pandas, Scikit-learn, Statsmodels), Excel, and MATLAB for regression, forecasting, and model validation. Learn reproducible research using Git/GitHub and R Markdown
3. Cyber Security
Gain deep insight into ethical hacking, secure network design, and threat mitigation strategies.
- Network Simulation & Ethical Hacking – Create simulated networks using GNS3
- Offensive Security Tools – Use Kali Linux, Nmap, Nessus, and SQLMap for penetration testing and vulnerability scanning
Students graduate with both theoretical and hands-on knowledge of contemporary cybersecurity practices.
4. Computer Science & Software Development
Learn to build, test, and deploy software and immersive digital systems using modern development tools.
- AR/VR Development – Design 3D environments using Meta Quest VR headsets
- Programming & Web Technologies – Learn Java, Python, C++, React, Vite, Supabase, and Vercel
- Network Simulation – Use Cisco Packet Tracer for networking fundamentals
- Next-Gen Networking – Get hands-on with Free5GC and UERANSIM to explore 5G architecture
- Operating Systems & Embedded Projects – Build real-world applications using Raspberry Pi
- Software Engineering Ethics & Principles – Explore responsible development practices, professional standards, and real-world deployment considerations
Research & Innovation Infrastructure
Our students and staff benefit from cutting-edge infrastructure that supports hands-on experimentation and research.
- High-Performance Computing (HPC) – Tackle data-intensive tasks like simulation, optimisation, and modelling
- GPU Cluster Access – Train deep learning models with scalable resources using NVIDIA RTX 2080Ti GPUs
- Robotics Platforms – Work with Niryo collaborative robotic arms to develop solutions in automation, AI, and mechatronics
This advanced setup empowers students to contribute to impactful research while building real-world expertise.