Are you passionate about the intersection of artificial intelligence and mechanical engineering in a real-world setting? Then you could join SKF’s Technology Development as...

Industrial Ph.D. – AI-driven product design automation

At SKF Technology Development, we strongly believe that introducing new technological advances in our current engineering processes can open new spaces for innovation. By evolving our solution space, we can better and faster tailor the correct solution to the customer’s technical needs and requirements and create better products and processes for tomorrow.

In collaboration with Linnaeus University, SKF is looking for an industrial Ph.D. student to work on the topic of using artificial intelligence to automate engineering design and product development. The successful candidate will join a multidisciplinary team of researchers and engineers who are developing innovative solutions for the design and optimization of mechanical systems. The goal of the project is to apply state-of-the-art techniques to automate the design process of various components and products, such as bearings, seals, and lubrication. The project will involve both theoretical and practical aspects.

About the job

As an AI Industrial Ph.D. within this project, you will be at the forefront of leveraging the power of machine learning and AI and make an important contribution to advance our engineering and design approaches and enable engineers and designers to create better products and services across various industries.

You will work closely with our business leaders, product development community, and university to develop AI solutions that enhance design processes and services. You will be part of reinventing the current way of working by using new tools and technologies to drive towards the concept of Automated design.

About your tasks

• explore and identify applicable data sources such as design rules, current designs, and testing data
• analyze large amounts of data, both structured and unstructured
• create new solutions and strategies for engineering problems
• work with team members and leaders to develop a strategy to validate AI techniques
• to discover trends and patterns, combine various algorithms and modules for engineering purposes
• validate and pilot solutions using various techniques and tools
• construction pilot solutions for the actual bearing design process

About you

To be successful in this role, we see that you have experience and/or education in the following topics:

• mechanical engineering
• statistical analysis and computing
• machine learning
• deep learning
• large language modeling
• processing large data set
• data visualization
• data wrangling
• mathematics
• programming

The industry Ph.D. student will be employed at SKF Technology Development located in Sweden (Gothenburg). Travel to Linnaeus University (Växjö) will be required. Additional periodical traveling might be required.

Requirements from Linnaeus University

The industry Ph.D. student will be enrolled in the graduate program at Linnaeus University’s industry graduate school on Data Intensive Applications (DIA, https://lnu.se/DIA). Therefore, the following academic requirements need to be fulfilled.

General entry requirements

• has been awarded a second-cycle qualification
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle or has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

Specific entry requirements

• approved courses of a minimum of 90 credits in the subject of Computer and Information Science or the equivalent
• an individual study project of a minimum of 15 credits in the subject of Computer and Information Science, or the equivalent.

Assessment criteria

Good programming skills (for example Java, C#, Ruby, or Python), and solid training in mathematics and theoretical computer science. Documented expertise and working experience within at least two of the following research and educational areas is a great advantage:

• Big data analytics
• Data visualization
• Machine learning
• Predictive Maintenance
• Parallel processing

Because of the interdisciplinary character of the project and LNUC DISA (https://lnu.se/en/disa), the candidate should be an enthusiastic person who is interested in making sense of large and complex data sets of various types as well as capable of working both independently and within a group. Teamwork experience should be documented in the application. Professional proficiency in written and spoken English is required.

Start date: August 2024

Duration: 4-5 years

Location: Gothenburg, Sweden

Salary: According to SKF's regulations

Application

If you are interested and fulfill the criteria, please submit your application no later than July 12. Please include a cover letter, a CV, and a transcript of records in your application.

For questions about the Ph. D., please contact Linda Örtlund, Manager of Architecture, AI and Developmen Principles, at [email protected].

For questions about the recruitment process, please contact Danijel Sjögren, Recruitment Expert EMEA, at [email protected].