November 25, 2024

Master Thesis: Exploring Advanced Machine Learning Strategies

Research and Development Atlas Copco Sweden Stockholm

Your future job

Our solutions are a key part of most industries - electronics, medical research, renewable energy, food production, infrastructure and many more. Working with us means working with the latest technologies and groundbreaking, sustainable innovations.

Join us on our journey for a better tomorrow.

Education requirement: Master’s program in Computer Science, Physics, Mechatronic Engineering, Data Science, or related engineering disciplines. 

Level of thesis project: Master thesis 

Number of people needed:

Functional area: Atlas Copco, Tightening Technique and Lab Development 

Supervisors: Kajsa Hjort,Software Engineer and Wojciech Mateusz Hanus, Data Scientist.

Are you passionate about machine learning and eager to apply your skills in a real-world industrial setting? Join our team at Atlas Copco for an exciting Master's Thesis!  

Target

Develop a robust machine learning algorithm to process sensor data from screwdrivers. The solution should accurately identify x and y coordinates of interest, even in the presence of noise or data where such points may be absent. The thesis will include literature studies, algorithm development, and performance evaluation. Findings from this research will be used to recommend improvements to existing solutions. 

Mission

  • Literature Review: Identify best practices and innovative approaches in machine learning for sensor data analysis. 

  • Algorithm Development: Design models incorporating machine learning techniques tailored to sensor data characteristics and physics-based constraints. 

  • Performance Analysis: Compare the newly developed models with the current algorithm, highlighting strengths and areas for improvement. 

  • Collaborative Research: Work closely with our team to address challenges and share insights. 

  • Final Presentation: Deliver a comprehensive summary of findings, with actionable recommendations to refine the current solution.

Your Qualification: 

We invite you to apply if you have a passion for machine learning, analytical problem-solving, and collaborative research. We are excited to review your application and explore the unique perspectives and skills you will bring to our team. 

Experience or interest in the following areas is highly valued: 

  • Machine learning algorithms (e.g. Convolution Neural Networks). 

  • Physics-Based Modeling. 

  • Programming in Python. 

  • Familiarity with PyTorch, scikit-learn, or similar libraries. 

  • Analytical skills, able to approach complex challenges thoughtfully and independently. 

Company presentation: 

Atlas Copco’s Industrial Technique business area provides industrial power tools and systems, industrial assembly solutions, quality assurance products, software, and services through a global network. The business area innovates for sustainable productivity for customers in the automotive and general industries, maintenance, and vehicle service. Principal product development and manufacturing units are in Sweden, Germany, the United States, the United Kingdom, France, Japan, and Hungary. We provide opportunities for continuous learning and mentorship, ensuring a supportive environment for professional growth. 

At Atlas Copco we are committed to fostering an inclusive and diverse work environment where all individuals are encouraged to thrive. 

Practical information
Last day to apply: December 8th
Location: Sickla, Stockholm, Sweden 

Application instructions

Please send us your CV and an answer to the question in the application form. Due to General Data Protection Regulations (GDPR) we only handle applications received through our website. We look forward to your application! 

If you have any questions about the role, please contact Kajsa Hjort at [email protected]

Functional area: Research and Development
Country: Sweden
City: Stockholm
On-site/remote: On-Site
Brand: Atlas Copco
Company Name: Atlas Copco Industrial Technique AB
Date of Posting: November 25, 2024
Last day to apply: December 8, 2024