Lecturer in Software Engineering

We are looking for highly skilled and motivated individuals to join our team as Lecturers in Software Engineering. Successful candidates will have a strong academic background and expertise in the fields of in software engineering, machine learning, and signal processing for wearable technologies. 

The role includes teaching undergraduate courses, engaging in research activities, and contributing to departmental growth.

-          Deliver lectures and practical sessions in software engineering, machine learning, and signal processing for wearable technologies.

-          Develop and update course materials that reflect current research and industry trends in wearable devices.

-          Supervise undergraduate students in their academic work and research projects.

-          Collaborate with colleagues on research initiatives related to wearable technology development.

-          Participate in departmental and university service activities.

 

Algorithm Development: Expertise in developing AI-driven algorithms for physiological data analysis, specifically in signal processing for PPG-based measurements. Expertise in feature extraction, anomaly detection, and predictive modeling using machine learning.

Pattern Recognition and Data Processing: Skilled in time-series data analysis, including filtering, noise reduction, and pattern recognition, with strong experience in Python, MATLAB, or R. Able to accurately analyze physiological signals such as heart rate variability and oxygen saturation from PPG data.

Deep Learning Frameworks: Proficiency with TensorFlow, PyTorch, or similar frameworks for developing, training, and optimizing models for continuous, real-time health monitoring.

Cross-Disciplinary Collaboration: Effective communicator who works closely with hardware engineers to validate data accuracy and ensure seamless integration of AI algorithms on wearable platforms.

Firmware Development: Strong experience in developing firmware for wearable devices with a focus on real-time data acquisition and processing. Proficient in C/C++ and Python for Raspberry Pi or Arduino, ensuring efficient integration with PPG sensors.

Signal Processing and Embedded AI: Expertise in implementing low-power signal processing algorithms tailored for resource-constrained devices, managing data acquisition, processing, and storage to maximize battery efficiency and performance.

Algorithm Optimization and Deployment: Skilled in optimizing AI models for real-time performance on embedded platforms, working closely with hardware engineers to ensure smooth interaction between sensor data and AI-driven insights.

Testing and Debugging: Strong troubleshooting skills, including unit testing, validation, and calibration of algorithms in wearable devices to ensure accuracy and reliability in real-world conditions.

Together, they will create an integrated software system for a functional, PPG-based wearable prototype.