Over the last 3 years imec.iChange collected an extensive dataset (1002 people) on stress in the work environment. Data types include activity, mood, lifestyle log, stress annotations and physiological data from wearables. Additional to our internal development on algorithms for stress detection, we are looking for creative and novel ways of interrogating our dataset and uncover new drivers of stress.
The student will perform assignments on the analysis of the dataset, propose innovative data-driven research questions and mine our dataset to test hypothesis.
This investigation will have as outcome novel algorithms aimed at explaining divers aspects of stress responses in the population of our dataset. This work will be integrated into the digital phenotyping framework of imec.iChange.
- Study the problem & provide state-of-the-art analysis.
- Propose a research hypothesis on the dataset, apply and validate a technical solution – algorithmic technique.
- Compare the proposed solution with other methods.
- Organize and document dataset.
- Pre-process the data and apply descriptive statistics to data.
- Deliver documented code.
- Write technical documentation and conference paper.
- Present results to the team (intermediate and final).
- Msc Biomedical Engineering or Computer Science.
- Available for 9 to 12 months.
- Proficient practical knowledge of python (R is a plus).
- Familiarity with heterogenous data types, wearables data and machine learning.
- Familiarity with statistical methods.
- Analytic and critical thinking.
- Able to work independently, provide unbiased report and proactively receive feedback.
- Motivated student eager to expand knowledge in the field.
- Good written and verbal English skills.
For all inquiries, please contact:
Ms Najat Loiazizi, HR Business Partner. Telephone number: +31 (0)40 40 20 675