Haytham Younus

Background

Dr. Haytham Younus brings over two decades of blended industrial and academic experience to the forefront of advanced mechanical engineering and artificial intelligence. He currently leads a high-impact Knowledge Transfer Partnership (KTP) project between Cranfield University and Nissan Technical Centre Europe, serving as a Digital Vehicle Test Engineer to drive innovation in digital testing environments and semantic knowledge modeling.

His extensive career bridges severe real-world industrial environments with cutting-edge digital transformation. He spent twelve years with a major Caterpillar Inc. dealer as a Technical Engineer and Analyst, spearheading diagnostics, troubleshooting, new product introductions, and preventative maintenance planning for heavy machinery and power systems. He subsequently transitioned into technical academia and vocational leadership, spending nearly four years as a Senior Vocational Technical Instructor and Lecturer at ITQAN Technical College, where he collaborated with industrial giants like Aramco, Saudi Steel, and SEC to align workforce development with rigorous industrial standards.

Dr. Younus holds a BSc in Mechanical Power Engineering from Alexandria University and an MSc with Distinction in Advanced Mechanical Engineering from the University of Bradford. He successfully defended his PhD in Mechanical Engineering and Artificial Intelligence at the University of Bradford. Sponsored by a leading French automotive Tier 1 manufacturer, his doctoral research introduces an augmented intelligence framework designed to automate Failure Mode and Effects Analysis (FMEA). By unifying machine learning, surrogate modeling, and ontology engineering frameworks, his methodologies seamlessly merge disconnected design-phase data with field failure data to maximize product reliability and optimize lifecycle risk assessment.

As an educator and consultant, he spent over three years as a Mechanical Engineering Demonstrator at the University of Bradford, delivering advanced training in Model-Based Systems Engineering (MBSE), system optimization, and Python-driven data pipelines. A proven collaborator within the global engineering ecosystem, he has designed and delivered advanced technical workshops for engineers at world-class organizations, including Renault, Valeo, and Airbus. Through these initiatives, he equips corporate teams with the machine learning and simulation tools necessary to resolve complex reliability and system engineering challenges, permanently modernizing traditional engineering workflows into intelligent, data-driven systems.

Research opportunities

Machine learning, system engineering, knowledge graphs, ontology engineering, knowledge modeling, model-based systems engineering (MBSE), deep learning, digital twins, digital transformation, FMEA automation, predictive diagnostics, risk assessment methodologies, surrogate modeling, heterogeneous data fusion, and signal processing for engineering systems

Publications

• Articles in journals