Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

Bosch R&D Center Lund stands for modern development in cutting edge technology in the areas of connectivity, security, mobility solutions and AI. We are growing rapidly and looking for people to join us on our mission to become the Bosch Group’s 1st address for secure connected mobility solutions. We are working on a range of interesting projects, with a particular focus on software development for the automotive industry, electrical bicycles and home comfort.

Job Description

This master thesis proposes the application of machine learning algorithms to construct a dynamic model of a heat pump, aiming to capture its complex behavior and identify key factors influencing its performance and longevity. The research employs machine learning algorithms and exploratory analysis to uncover the relationships between heat pump properties and both dynamic behavior and lifespan for selected features.

The initial phase involves utilizing machine learning algorithms to model the heat pump's dynamic behavior. These data are stored in a graph database, providing a structured and salable framework for efficient querying and knowledge extraction.

Subsequently, exploratory analysis is applied to discern patterns and correlations within the stored data. This analysis aims to identify which heat pump properties significantly impact dynamic behavior and contribute to its overall lifespan. The results are then incorporated back into the graph database, facilitating streamlined grouping and retrieval for future inquiries.

In summary, this research employs machine learning techniques for dynamic modeling of a heat pump, leveraging a graph database for efficient data storage and retrieval. The subsequent exploratory analysis sheds light on the influential factors affecting both the heat pump's dynamic behavior and its lifespan. This holistic approach enhances our understanding of heat pump systems and lays the groundwork for informed decision-making in optimizing their performance and longevity.

Proposed solution and scope of the master thesis project
The preliminary structure of the project is made up of three parts:

a. Study

• Review existing literature to understand the current state of knowledge.
• Consult with advisors to refine and narrow down the research focus.
• Conduct an in-depth review of relevant literature.
• Establish the theoretical framework for solution options and proposal for phase 2

b. Setup a POC with existing project data and explore example use cases with selected tooling based on data and framework from already finished master thesis.

c. Evaluation

• Present the research findings clearly and concisely.
• Discuss the of the results in the context of the literature review and the POC outcome. Address any unexpected or contradictory results.
• Suggest avenues for future research in the area

Scope of master thesis project
Two students completing 30 credits each (20 weeks) onsite at the Lund office

Qualifications

Please note: Only applications from students at a Swedish University are accepted.

You should be familiar with machine learning concepts/algorithms/models and have a strong programming background. Knowledge in graph technologies is beneficial.