IAESTE Internships

Location

India

Internship type

ON-SITE

Reference number

IN-2026-B5214-KU

Students Requirements

General discipline

Computer Science / Informatics

Completed Years of Study

3

Fields of Study

General
Computer and Information Research

Languages

English Excellent (C1, C2)

Required Knowledge and Experience

-

Other Requirements

The intern should have a solid understanding of Python programming and a basic grasp of deep learning concepts

Work Details

Duration

12 - 12 Weeks

Within These Dates

04.05.2026 - 04.09.2026

Holidays

NONE

Work Environment

-

Gross pay

10000 INR / month

Working Hours

40.0 per week / 8.0 per day

Living Lodging

Type of Accommoditation

IAESTE- LC KARUNYA

Cost of lodging

5000 INR / month

Cost of living

8000 INR / month

Work Offered

Additional Info

1. The option to work from home is available for this offer. In this case, the available dates for the internship are from May 2026 - September 2026 and there will be no stipend provided.However, if the intern chooses to work at the employer's location, a stipend will be provided based on the dates specified in the 'Work Offered Field'.2. The intern is required to fill out the attached declaration form to confirm their preferred mode of internship.

Work description

AI-Powered Wearable Signal Processing for Atrial Fibrillation DetectionOverview:This internship at the IBM Center of Excellence focuses on developing a next-generation atrial fibrillation (AF) detection system using photoplethysmography (PPG) signals from wearable devices. Interns will explore deep learning, GAN-based signal reconstruction, and Explainable AI to address motion artifacts, signal issues, and enhance detection accuracy, interpretability, and clinical trust for deployment.Objectives:1) Apply GAN techniques for PPG signal reconstruction and dataset augmentation to improve robustness.2) Integrate Explainable AI to derive clinically interpretable rules for AF classification.3) Validate the detection framework using wrist PPG datasets and matched ECG recordings.Outcomes:1) Proficiency in applying deep learning to biomedical signal processing and health informatics.2) Hands-on experience with GANs for data enhancement, reconstruction, and augmentation.3) Understanding of Explainable AI methods for transparent and trustworthy healthcare applications.Intern's Responsibilities:1) Preprocess, clean, and analyze wearable PPG datasets using Python, machine learning, and data science tools.2) Implement GAN-based methods to improve signal quality and dataset diversity.3) Develop and evaluate Explainable AI models ensuring transparent, clinically relevant AF detection.

Deadline

24.04.2026