Research Associate – Research Fellow in Human-Centric AI for Manufacturing

Organisation

Since its foundation in 1614, the University of Groningen has enjoyed an international reputation as a dynamic and innovative centre of higher education offering high-quality teaching and research. Belonging to the best research universities of Europe and joining forces with prestigious partner universities and networks, the University of Groningen is truly an international place of knowledge.

Faculty of Economics and Business
The Faculty of Economics and Business (FEB) has an inspiring study and working environment for students and employees. International accreditation enables the Faculty to assess performance against the highest international standards. It also creates an exciting environment of continuous improvement. FEB's programmes, academic staff and research do well on various excellence ranking lists.

Job description

The position is part of the multi-disciplinary research project ‘STAR: Safe and Trusted Artificial Intelligence for Future Manufacturing Lines’, funded by EC (European Commission). More details on STAR are available at

https://star-ai.eu

.

The research at RUG is led by Christos Emmanouilidis from the Faculty of Economics and Business. The STAR project is a joint effort of AI and digital manufacturing experts, aiming to deploy standards-based secure, safe, reliable and trusted humancentric AI systems in production systems. Artificial intelligence (AI) systems in the manufacturing sector are increasingly replacing human tasks improving the automation of production but in other cases open-up possibilities for increased co-operation between human and non-human technology actors (for example cobots, AI agents). These systems need to be safe, trusted and secure, even when operating in dynamic, unstructured and unpredictable environments to be able to react to different situations and security threats. Ensuring the safety and reliability of these systems is a key prerequisite for deploying them at scale and for fully leveraging the benefits of AI in manufacturing. The project involves major manufacturing companies, cutting edge technology providers, including AI solutions developers, legal / ethics experts, and research organisations across 11 countries in a consortium that incorporates key stakeholder types that need to be involved for the successful design, development, deployment and operation of AI solutions in this sector. This level of involvement enables the STAR approach to bring together contributions from multiple perspectives, including Engineering, Computer Science, Technology & Operations Management, Human Factors in Socio-Technical Systems, Ethics and Legal Aspects of Technology Adoption, and Innovation Management.

Research fellow / research associate role

The researcher will contribute to the research of RUG within the STAR project and in particular in the following activities:

1. Research on applying Machine Learning for AI – enabled solutions for STAR project targeted problems, namely:

• vision-based quality inspection (aimed use: Human-Robot Collaboration for Quality Management)
• workers/operator’s state recognition (aimed use: human digital twin)
• human movement prediction in joint human – robot co-existence work environment spaces (aimed use: Human Behaviour Prediction and Safety Zones Detection in human-cobot collaboration).
2. Contribution to Worker’s Training and Continuous Learning regarding the use of AI-enabled solutions via a dedicated training platform, made available through the project.
3. Socio-economic Evaluation approach and its application on the STAR use cases.

You will be part of a highly dynamic and engaging consortium team offering exciting multi-disciplinary synergies and opportunities to develop and deploy your skills while engaging with the project partnership and contributing to research on Human-Centric AI. You will be expected to contribute to and lead in some cases project research and technical reporting, dissemination activities, and experimental empirical investigation of machine learning on project-related data. You will also engage in project regular meetings and contribute to project co-creation and evaluation workshops together with the STAR industrial use case partners, with engagement from other STAR partners and external stakeholders, demonstrating own initiative.

The position is assigned for a period of 18 months (1.0FTE) and can commence by June 1 2022. The postdoc will be located in the Department of Operations, Faculty of Economics and Business, but will also work together with members of the consortium involved in the broader research project.

Qualifications

• PhD in Engineering, Computing/Data Sciences, or Industrial Engineering / Management (completed or near completion) (preferable)
• MSc in in the above fields (completed or near completion)
• competences in data analytics, machine learning / AI (for example in Python and relevant libraries, including machine learning libraries such as ScikitLearn, Keras, Tensorflow etc), including interest and ability to expand such competences with active experimental research
• experience with programming for data management and data integration would be an advantage (on top of Python data management and machine learning): JSON/MQTT for data exchanges, noSQL (MongoDB) and Influx DB for data (and time series data) management.
• excellent technical and research report writing skills; a relevant publications’ track record will be an advantage.

Conditions of employment

We offer you in accordance with the Collective Labour Agreement for Dutch Universities:

• a starting salary depending on qualifications and work experience between € 2,709 (salary scale 10 Dutch Universities) and € 4,978 gross per month (salary scale 11) based on a fulltime position
• the fulltime (1.0 FTE) appointment is temporary for a period of 18 months. In addition, the university offers a 8% holiday allowance and a 8.3% end of year bonus.

Starting date: as soon as possible.

Information

For information you can contact:

Prof. Christos Emmanouilidis,   [email protected]

(please do not use the email addresses above for applications)