Real-time manufacturing by Artificial Intelligence
[Closed]
Work description
The activities to be developed fall within the scientific field of Industrial Engineering and Management, specifically in the sub-area of Industrial Production Systems. The work will focus on the domains of real-time manufacturing, integrating Artificial Intelligence technologies such as machine learning, predictive analytics, and prescriptive analytics, with the aim of improving productivity, optimizing resource use and consumption, and increasing quality levels in manufacturing processes. Traditional manufacturing systems often exhibit inefficiencies and a lack of agility. The integration of Artificial Intelligence into manufacturing operations represents a transformative opportunity to unlock new, more adaptive, productive, and responsive ways of working, thereby enhancing competitiveness. By incorporating AI technologies—such as machine learning, predictive, and prescriptive analytics—into the manufacturing environment, the goal is to dynamically adapt production in real time, enhance productivity, minimize downtime, reduce waste, and improve manufacturing quality indicators. The core challenge is to develop comprehensive analytical methods that are flexible enough to address the specificities of various manufacturing processes and environments. More specifically, the research will focus on applied problem-solving in real-time production, including continuous performance monitoring in industrial settings, adaptive production planning supported by intelligent algorithms, integrated management of intralogistics and external logistics systems based on digital models and digital twins, optimization for robotic and automation systems, and predictive maintenance of assets. Additionally, the development of optimization models integrated with Artificial Intelligence will be explored, capable of managing uncertainty and variability in production systems. All these activities will be aligned with the guiding principles of Industry 5.0, fostering augmented intelligence, personalization, and sustainability in advanced industrial production systems.
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.014, described above.
Minimum profile required
Candidates should possess a hybrid profile, with solid expertise in manufacturing (e.g., processes, technologies, systems, automation, among others) and strong analytical capabilities, particularly in advanced analytics and artificial intelligence. They must demonstrate proven experience in the development and implementation of AI solutions for real-world applications, preferably in industrial or manufacturing contexts, as well as hands-on experience in data collection, pre-processing, and analysis techniques. Overall, ideal candidates should combine technical knowledge, practical experience, and strong interpersonal skills to drive research and innovation and to deliver tangible outcomes.
Application Period
Since 18 Jul 2025 to 31 Jul 2025
[Closed]
Centre
Industrial Engineering and Management