Assistant Lecturer in Software Engineering

Job Description

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.

Key Responsibilities:

Assist in the delivery of undergraduate courses related to Software Engineering, Computer Science, Electrical Engineering, or a closely related field.
Support the development of course materials and learning resources.
Mentor students in their academic and project work.
Contribute to ongoing research projects related to wearable PPG devices.
Assist with laboratory sessions and student assessments.

Job Requirements

Qualifications:

Master’s degree in Software Engineering, Computer Science, Electrical Engineering, or a closely related field.
Enthusiasm for teaching and engaging with students.
A strong foundation in signal processing, circuit design, or wearable device technologies.
Willingness to collaborate with colleagues and contribute to research projects.

1.Machine Learning and AI Engineer

Skills and Expertise

· 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.

2.Embedded Software and Signal Processing Engineer

Skills and Expertise

· 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.