Embedded Faculty

A faculty position in Embedded Systems typically involves teaching courses related to embedded systems design, development, and applications, conducting research, and mentoring students. Responsibilities include curriculum development, delivering lectures and labs, supervising student projects, participating in departmental and university service, and potentially pursuing external funding for research.  Key Responsibilities: Teaching: Delivering lectures and conducting laboratory sessions in embedded systems related courses.  Developing and updating course materials, including syllabi, assignments, and assessments.  Supervising student projects, including senior design projects and theses.  Providing guidance and support to students in their learning and research endeavors.  Service: Participating in departmental and university committees.  Mentoring and advising students, including those pursuing graduate degrees.  Engaging with the broader academic and professional community in the field of embedded systems.  Curriculum Development: Contributing to the development and improvement of the embedded systems curriculum.  Staying current with the latest trends and technologies in embedded systems.  Exploring and integrating new teaching methodologies and technologies.  Required Qualifications: Experience: Experience in teaching and research in embedded systems, including experience with relevant hardware and software tools. Skills: Strong programming skills (e.g., C/C++), knowledge of microcontrollers and microprocessors, understanding of real-time operating systems (RTOS), and experience with embedded system development tools.  Key Areas of Expertise: Embedded Software Development: Proficiency in programming embedded systems using languages like C/C++, assembly language, and experience with RTOS.  Hardware Design and Interfacing: Understanding of microcontroller and microprocessor architectures, hardware interfaces (e.g., I2C, SPI, UART), and experience with circuit design and schematic reading.  Real-Time Systems: Knowledge of real-time operating systems and their application in embedded systems, including task scheduling, resource management, and synchronization.  Embedded AI: Experience in developing and deploying AI algorithms on embedded platforms, including machine learning and deep learning models.  Cyber-Physical Systems: Understanding of the interaction between embedded systems and the physical world, including sensors, actuators, and control systems.