Context

To optimally run industrial processes, it is key to use both AI and human operators' knowledge. In the process industry, deciding on the optimal settings requires experience and understanding of the process. The complex interplay between many internal and external factors makes it difficult for human operators to decide in real time what the optimal settings are based on all available data. Machine learning and AI can be used to make predictions about optimal settings, but final decisions and responsibilities remain for a large part with human operators

Project Description

In the current developments towards human-centered Industry 5.0 we are looking for ways to include AI in the daily work of human operators. This project will specifically study how process operators can optimally interact with AI to make better informed decisions on process settings. To do this, we will explore different data-analytic (AI) models that can predict process settings, balancing model explainability and predictive performance. Multiple sources of data will be integrated into the predictive models to predict various aspects of the manufacturing processes. Next to studying human-AI interaction in simulated processes, you will explore how the interaction should be designed for optimal useability.

Job Description

As a PhD candidate, you will be responsible for doing several behavioral studies into human-AI interaction together with the supervising team (dr. Geert van Kollenburg, dr. Lijia Tan, and dr. Rob Basten). Your research will be on an intersection between data science (Machine learning/AI), behavioral sciences and process analytics. Next to direct interactions with industrial partners, you will write your results in papers for academic journals and will visit conferences related to your work.

Academic and Research Environment

The project and the team of professors are part of the Operations, Planning, Accounting, and Control group (OPAC). OPAC specializes in various aspects of operations research, operations management, logistics, and optimization. This diverse group has many social activities and currently hosts around 50 PhD students. You will work together with other (PhD) researchers in the domain of Data Driven Decision Making.

• We are looking for a candidate who wants to work and develop in a multidisciplinary setting.
• A master's degree in social and/or behavioral research methods, operations research, data science, econometrics, computer science or related fields. Applicants graduating the current academic year are welcome to apply.
• Strong competence in statistics/data analytics and familiarity with programming languages like Python or Julia.
• Expertise in questionnaire design and/or qualitative research methods.
• Excellent verbal and written communication skills to collaborate in an international setting.
• Fluent in spoken and written English (C1 level or higher).
• Ability to work in an interdisciplinary team and collaborate with industrial partners.
• Affinity to learn Dutch. Dutch proficiency is a pre, but not required at the start.
• Motivated to develop your teaching skills and coach students.
• Experience with Machine Learning and AI models is a pre.
• Experience with Java, Javascript, or related languages is a pre.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.