Thesis work, 30 credits - MOCAP maintenance in VR for pharmaceutical manufacturing

Are you a passionate student eager to make an impact? We’re thrilled to announce a unique thesis collaboration between AstraZeneca and KTH (Kungliga Tekniska Högskolan).  

About AstraZeneca:  

AstraZeneca is a global, science-led, patient-centered biopharmaceutical company focusing on discovering, developing, and commercializing prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit.   

Thesis work description:    

Background: In this project, you will focus on researching, producing and comparing motion capture (MOCAP) visualization methods of workers performing maintenance tasks in a manufacturing line. For this, you will explore different types of MOCAP systems (high-fidelity vs low-fidelity), and integrate these into a Virtual Reality (VR) replica of the real manufacturing line, testing different augmented visualization methods for them. 

Purpose: The main objective is to improve the training of newly hired employees of a packing line within AstraZeneca, as they learn to operate and maintain the machinery. We aim to achieve the objective by proposing and comparing visual/audio/tactile augmentations to the MOCAPed visualizations produced. 

The project has a number of aims: 

• Explore the cost-benefit differences between HIFI and LOFI MOCAPed visualization and how to augment them. 
• Understand the basic concepts of smart manufacturing and predictive maintenance and how to apply them. 
• Compare the proposed visualizations in VR user experiments with novice users. 
• Identify new possible improvements, for example reducing the time and cost of producing MOCAPed visualizations and improving the learnability of novices. 
• Contribute to the AZ-KTH partnership project on “Smart Predictive Maintenance” 

Overall Project description: 

Maintenance of manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilizes condition monitoring data to predict the future machine conditions for decision making. Implementation of effective prognosis for maintenance brings increased system safety, improved operational reliability, increased maintenance effectiveness and OEE (Overall Equipment Effectiveness), reduced maintenance inspection and repair-induced failure, and reduced lifecycle Cost. Initial focus is to use physical modelling of the normal behavior based on underlying specifications allowing the detection of deviations as anomalies. Moreover, the expectation is to build hybrid kinds of models combining physical models with machine learning algorithms. In order to put the work in its (academic) context, a state-of-the-art review needs to be done. The work is presented in a final report, including an outlook for future work. The student undertaking this thesis will focus on the immersive visualization side of the project, collaborating with the overall team. 

Mentorship and Collaboration:  

The work can be carried out by one or two degree workers.  

Renan Guarese (HCI Postdoctoral Researcher at KTH) will be the main supervisor, working under Fabian Johnson (Senior Business Developer at AZ) and Mario Romero (Associate Professor at KTH). 

Location:  

• Time split between AstraZeneca, Södertälje and KTH.  

Requirements:

• Ongoing Master Studies at KTH 

So, what’s next?   

Apply today and take the chance to be part of making a difference, making connections, and gaining the tools and experience to open doors and fulfill your potential. We can't wait to hear from you!   

We welcome your application as soon as possible, but ahead of the scheduled closing date 15th of November 2024. In the event that we identify suitable candidates ahead of the scheduled closing date, we reserve the right to withdraw the vacancy earlier than published.   

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.