Projeto PRR

Sumário

The integration of data science methods into industrial engineering represents a significant opportunity to improve the efficiency, agility and competitiveness of industries. Traditional processes are often inefficient and lack the adaptability required in today´s dynamic markets. By leveraging data science techniques such as machine learning, predictive analytics and prescriptive analytics, it is possible to transform industrial engineering practices in several areas. For example, increased efficiency translates into reduced production costs and improved resource utilization, ultimately improving the organizations´ competitiveness position. The agility provided by real-time optimization enables industries, providing goods and services, to adapt quickly to changing market conditions, customer preferences and unforeseen disruptions. In addition, the ability to predict and prevent downtime minimizes disruptions to production schedules, ensuring consistent output and customer satisfaction. Furthermore the focus on improving quality throughout the manufacturing process can lead to fewer defects, higher customer satisfaction and improved brand reputation.The position of "Assistant Researcher for Data Science in Industrial Engineering" will play a critical role in advancing the understanding and application of data-driven approaches in industrial engineering environments within the Industrial and Systems Engineering Domin at INESC TEC. This position is essential for this domain and respective research centers to maintain its status as a leading international authority in industrial engineering, capable of integrating cutting-edge technologies such as data science into research activities. This position will not only contribute to the advancement of knowledge within the institution but also empower other researchers with the skills and expertise needed to thrive in the Industry 4.0 era.The Assistant researcher will be responsible for developing comprehensive analytical methods tailored to the specific requirements of different processes and environments. This includes, for example, identifying relationships between process variables and performance metrics, such as defect rates, machine processing speeds, or customer retention, which will be essential to enable real-time optimization. In addition, the Assistant researcher will focus on promoting the integration of data science methods into Manufacturing Execution Systems (MES), Service Management Systems (SES) and Customer Relationship Management (CRM) systems to improve data collection, integration and real-time monitoring capabilities. This will enable adaptive planning, quality management and continuous improvement initiatives in diverse contexts.Candidates for this position should possess a hybrid profile, combining strong business acumen with deep expertise in industrial engineering and advanced analytics. Ideal candidates will hold a PhD in Engineering, Computer Science, Data Science, Industrial Engineering, or a related field. They must demonstrate hands-on experience in developing and implementing data science solutions within industrial settings, with a strong foundation in data collection, preprocessing, and analysis techniques. Overall, the ideal candidates should possess a combination of technical expertise, practical experience, and strong interpersonal skills to drive research and innovation and deliver tangible results.To ensure the successful recruitment of talented researchers for the described position, an extensive communication campaign will be launched across multiple platforms and networks to reach a wide pool of potential candidates. INESC TEC can use its network of academic collaborators, industry partners and alumni to spread the word about the position and encourage qualified individuals to apply.