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. • 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 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) Work #LikeABosch starts here: Apply now! #LI-DNI