Sustainable and Integrated Operations Management with Artificial Intelligence
[Closed]
Work description
These activities will be carried out within the scientific field of Industrial Engineering and Management, specifically in the sub-area of Operations Management, with a particular focus on Sustainable and Integrated Operations Management. The work will leverage Artificial Intelligence (AI) techniques to enhance the efficiency, resilience, and sustainability of value chains. Industrial companies must revolutionize operations management by integrating sustainable practices both horizontally and vertically and by adopting advanced artificial intelligence techniques. This effort acknowledges the pressing need for companies to optimize operations while minimizing environmental impact, reducing the consumption of raw materials and resources, improving working conditions for operators, and strengthening resilience across the value chain—ultimately ensuring long-term sustainability. This holistic view of operations will enable: • Improved efficiency through integration, by implementing AI-based solutions to optimize operations horizontally across various functional areas (e.g., procurement, production, distribution, sales, etc.) and vertically throughout the entire value chain, from suppliers to end customers. • Environmental sustainability across value chains, by developing strategies to minimize environmental footprints and strengthen circularity not only within individual operations but also across the entire value chain—including suppliers, manufacturers, distributors, and retailers. • Risk mitigation through collaboration and information sharing among value chain partners, enabling the collective identification and mitigation of risks to ensure resilience and sustainability. • Decision support for integrated value chain management, by providing actionable insights and predictive and prescriptive analytics to enable proactive and integrated management that balances economic efficiency with environmental and social considerations.
Academic Qualifications
National, foreigner and stateless candidates holding a PhD in Industrial Engineering and Management, Data Science, or related scientific area, and who hold a scientific and professional CV that showcases a profile suitable for the position of Assistant Researcher and the position with reference 2023.14760.TENURE.015, described above. Candidates must possess a hybrid profile, with extensive experience in business models and strong expertise in operations management—including processes, technologies, systems, and automation—as well as robust analytical skills in advanced analytics and artificial intelligence. They should have a proven track record in the development and implementation of AI solutions for real-world applications, preferably in manufacturing or industrial environments, along with hands-on experience in data collection, pre-processing, and analysis techniques. Overall, candidates should demonstrate a combination of technical knowledge, practical experience, and strong interpersonal skills to drive research and innovation and deliver tangible results.
Minimum profile required
PhD in Industrial Engineering and Management, Data Science, or related scientific area.
Application Period
Since 18 Jul 2025 to 31 Jul 2025
[Closed]
Centre
Industrial Engineering and Management