Admission 2026

Biomedical Engineering

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Infrastructure and Facilities

The Department of Computer Science & Engineering (Artificial Intelligence and Machine Learning) and Master of Computer Applications is supported by a computing ecosystem designed for high-performance data processing, algorithm development, deep neural network training, and enterprise application engineering.

Our laboratory infrastructure provides high-speed networked computing systems, dedicated GPU acceleration, open-source AI frameworks, and industry-standard development suites to ensure students gain practical, end-to-end software and model engineering experience.

Infrastructure and Facilities

LABORATORY & COLLABORATIVE ECOSYSTEM

The department houses purpose-built, high-standard instructional laboratories, being designed to support student projects, experimental verification, and product development:Through industrial partnerships, hospital tie-ups and collaborative initiatives, students gain direct exposure to real-world clinical workflows, regulatory standards and medical device commercialization pipelines. The laboratory and collaborative ecosystem is planned to be built

  • Human anatomy and physiology laboratory
  • Biosciences laboratory
  • Biomedical instrumentation laboratory
  • Sensors and measurements laboratory
  • Diagnostic and therapeutic equipment laboratory
  • Medical image processing laboratory
  • Biomedical system modelling laboratory

Computing & Network Infrastructure

  • Hardware & Compute Power: Workstations with multi-core Intel i5/i3 architectures and dedicated graphics cards for running matrix computations, deep learning models, and local virtual machines.
  • Network Backbone: Dedicated high-speed campus-wide LAN and high-bandwidth Wi-Fi for uninterrupted access to cloud instances, GitHub repositories, and dataset downloads.
  • Operating Systems & Environments: Dual-boot flexibility supporting Windows 10/11 and Fedora/Ubuntu Linux across programming terminals.
  • Power Continuity: Centralized Online UPS backup system ensuring continuous operation during lab practicals, assessments, and computational model runs.