Master Thesis Ambient Sensing for Digital Health Biomarkers

Passive-ambient smart home sensors offer a unique privacy-preserving opportunity to continuously monitor health and functional independence in senior living. By analyzing subtle patterns in daily life (e.g. appliance use room transitions door movements) we can derive digital biomarkers to act as leading indicators for cognitive metabolic or physical health decline.

• As part of our research team you will dive into ambient sensing analyze real-world in-home datasets and help develop cutting-edge behavioral health scoring models.
• You will perform a comprehensive literature review on ambient sension for health applications.
• Furthermore you will preprocess and analyze passive sensor data (activity presence power usage) collected from our multi-home study cohort.
• You will research establish and validate mathematical scoring algorithms that condense meaningful insights from the data.
• Additionally you will investigate secondary behavioral biomarkers exploring concepts like nutrition/appliance tracking and sleep/mobility patterns.
• Moreover you will validate and showcase your algorithms in controlled environments including inside a health-centric tiny home.
• Finally you will document research findings code and evaluate results for internal presentations and potential scientific publication.

Qualifications :

• Education: Master studies in the field of Computer Science Data Science Medical Technology Biomedical Engineering Physics or comparable with a good academic record
• Experience and Knowledge: proficient in Python and common data science libraries (e.g. Pandas NumPy Scikit-learn); solid understanding of time-series analysis statistical modeling or machine learning; familiarity with smart home systems (e.g. Home Assistant) or sensor data processing is a plus
• Personality and Working Practice: you excel at analyzing problems methodically structuring your work systematically while independently driving projects toward key goals
• Work Routine: your on-site presence is required
• Enthusiasm: you show great enthusiasm for interdisciplinary digital health research and enjoy presenting complex results
• Languages: fluent in English

Additional Information :

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.

Need further information about the job
Jan Rudolph (Functional Department)
49 711 

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Remote Work :

No

Employment Type :

Full-time