Join Our Team at Fraunhofer ISST and Shape the Future of Healthcare with AI

At the Fraunhofer Institute for Software and Systems Engineering ISST in Dortmund, we are pioneers in applied cutting-edge research, dedicated to enhancing digital transformation across Europe. Our focus is on maximizing the potential of data, setting standards for a fair data economy, and enabling sovereign data management within various sectors including mobility, industry, and healthcare. Our mission is to innovate for a sustainable future in society and the economy both within Germany and across Europe.

Master's Thesis: Advanced Machine Learning and XAI for Heart Sound Analysis

In our Healthcare department, we're exploring the next generation of software technologies for data-driven healthcare solutions. This includes developing interoperable health data spaces and applications that optimize clinical processes and enhance mobile health apps and therapeutic algorithms. Our vision extends to data-driven healthcare processes and closed-loop precision medicine, particularly in areas such as Health Information Exchange, Health Applications and Analytics, and Personal Data Ecosystems.

An exciting example of our work is the automated analysis of heart sounds, which typically relies heavily on the subjective assessment of healthcare professionals. By integrating Machine Learning algorithms, we aim to provide an objective analysis that supports and enhances medical diagnostics.

The goal of this thesis is to utilize Machine Learning and Explainable AI (XAI) to improve heart sound analysis, potentially involving stethoscope position detection and recognition of systolic/diastolic sounds using XAI to illustrate findings. This project will utilize public, openly accessible data, with specific focus areas tailored to your interests.

What You Will Do

You will craft your thesis on the topic "Application of Machine Learning and XAI in Analyzing Heart Sounds", benefiting from our support and extensive expertise. Your project will involve:

• Researching state-of-the-art Machine Learning solutions.
• Exploring systematic audio data assessment.
• Developing and evaluating Machine Learning pipelines tailored to healthcare.
• Analyzing factors influencing model quality and decisions, potentially using Explainable AI.

What You Bring to the Table

Enrolled in a Master's program in Data Science, Computer Science, Medical Informatics, Medical Technology, or a relevant field. You should have:

• Basic knowledge of Supervised Machine Learning techniques.
• Initial practical experience with Python.
• A strong interest in the future of healthcare and Explainable AI.
• The ability to work independently and in a solution-oriented manner.
• Proficiency in German and English, both written and spoken.

What You Can Expect

You'll have the opportunity to write your thesis on a significant, future-facing topic at the intersection of science and industry. Work within an open, dedicated team with flat hierarchies, enjoying ample freedom to develop and implement innovative ideas. This position includes the option to work entirely remotely, with visits to our institute in Dortmund only necessary for initial and final administrative tasks. You will benefit from personal supervision and access to our extensive pool of knowledge.

The Fraunhofer-Gesellschaft is committed to promoting a diverse range of skills within our workforce and welcomes applications regardless of age, gender, nationality, ethnic and social origin, religion, disability, or sexual orientation and identity. Preference will be given to severely disabled candidates with equal qualifications.

This position is initially for a period of 6 months, with no financial compensation but offering comprehensive guidance and support for your master's thesis.

If you're ready to contribute to pioneering developments and scientific excellence, apply now with your detailed application documents at .

For any questions, please contact:

Pinar Bisgin
email:

Applications can only be accepted via our online application portal. Regrettably, we cannot consider applications sent by email or post.

Job Code: 73860

Application Deadline: