Machine Learning - Internship

Selected intern's day-to-day responsibilities include:

1\. Analyze and preprocess noisy, real-world health and sensor data using

statistical and signal processing techniques

2\. Design and implement signal cleaning, filtering, denoising, and feature

extraction pipelines

3\. Apply time-series analysis, spectral analysis, and statistical modeling to

interpret complex data patterns

4\. Develop and train machine learning models on preprocessed signals for

prediction, classification, or anomaly detection

5\. Evaluate data quality and identify artifacts, outliers, and

inconsistencies in raw datasets

6\. Collaborate with cross-functional teams to understand data sources, sensor

behavior, and real-world constraints

7\. Validate models using appropriate statistical metrics and experimental

design principles

8\. Optimize algorithms for performance, robustness, and scalability

9\. Document assumptions, methodologies, and findings clearly for technical

and non-technical stakeholders

10\. Iterate rapidly on experiments, incorporating feedback from real-world

data observations

11\. Stay updated with advances in signal processing, applied statistics, and

ML techniques relevant to healthcare data