Job
Description
About the Institution Situated amidst the tranquil greenery of Vandalur, Chennai, B.S. Abdur Rahman Crescent Institute of Science and Technology stands as a beacon of academic excellence since its establishment in 1984. With a rich legacy of over 40 years, the institution has progressively expanded its academic portfolio, currently offering 57 programs across 12 schools, including Ph.D. opportunities spanning all disciplines. As a burgeoning leader in higher education, the institute is dedicated to providing top-notch education, fostering strong industry connections, and creating exceptional placement avenues, all while nurturing an entrepreneurial spirit among its students. Embracing a diverse student body from around the world, our programs are meticulously crafted to equip students with the requisite knowledge and skills to realize their ambitions and flourish in their chosen careers. About the Department of Mechanical Engineering Founded in 1984, the Department of Mechanical Engineering has been a cornerstone of the institute from its inception. The department's programs hold accreditations from the National Board of Accreditation (NBA), underscoring its commitment to upholding the highest academic standards. With a profound focus on innovation, research, and industry partnerships, the department prides itself on preparing students for success in academia and the professional realm. Role Description We are in search of a highly driven PhD student to join our esteemed research team at B.S. Abdur Rahman University (Formerly Crescent Engineering College) in Chennai. This is a full-time, on-site, 4-year PhD program dedicated to advancing research in machine condition monitoring and predictive maintenance. As a PhD student, your responsibilities will include: - Engaging in cutting-edge research on machine condition monitoring, vibration analysis, and fault diagnosis. - Formulating and executing advanced data analysis techniques to evaluate machine health. - Disseminating research discoveries through publications in peer-reviewed journals and presentations at academic conferences. - Collaborating with faculty members, industry affiliates, and fellow researchers. - Contributing to teaching activities, mentoring undergraduate or master's students, and participating in departmental seminars. The Challenge In industrial settings, ensuring the reliability and efficiency of machinery is paramount to minimizing downtime and curbing maintenance expenses. Nevertheless, traditional maintenance methods often fall short in accurately predicting failures, resulting in unforeseen breakdowns and costly repairs. The primary scientific challenges in this research endeavor encompass: - Developing robust predictive maintenance strategies to optimize machine performance. - Understanding and mitigating wear, degradation, and failure modes in mechanical systems. - Exploring advanced condition monitoring techniques like vibration analysis, acoustic emissions, and thermal imaging. - Integrating machine learning and signal processing to bolster fault detection and diagnostics. - Embedding the devised framework into real-time IoT-based monitoring systems for continual condition assessment and decision-making. This PhD position offers a unique opportunity to address these challenges through experimental studies, data-driven approaches, and computational modeling over a span of four years. Your Profile We seek candidates who fulfill the following criteria: - Possession of a Master's degree in Mechanical Engineering or a closely related field with a track record of exceptional academic performance. - A keen interest in machine condition monitoring, predictive maintenance, and asset management. - Proficiency in signal processing, vibration analysis, and fault diagnosis, or a readiness to acquire these skills. - Independent problem-solving abilities, innovative thinking, and a hands-on research mindset. - Aptitude for working in a multidisciplinary research environment and fostering effective collaborations. - Strong analytical skills coupled with an engineering-oriented, goal-driven approach. - Experience or interest in IoT-based condition monitoring and real-time data acquisition. - Technical Skills: Proficiency in MATLAB, Python, AWS, IoT, Origin, and COMSOL. - Excellent written and verbal communication skills in English. For candidates interested in this opportunity, we encourage you to apply now or reach out for additional information! To apply, kindly forward your CV to Dr. Syed Shaul Hameed at syedshaulhameed@crescent.education. Want to Know More Should you have any queries regarding this PhD position, do not hesitate to contact: Dr. Syed Shaul Hameed Assistant Professor (Senior Grade) syedshaulhameed@crescent.education Department of Mechanical Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology For inquiries pertaining to the research scope, position specifics, or application process, Dr. Syed will be delighted to assist you. For general information, please refer to the brochure. Admission to Ph.D. - Crescent University,
