Shell.ai Hackathon 2025 | Apply Now
Role :
Student Edition General Edition Startup Edition
Location :
WORK FROM HOME
JOB DESCRIPTION:
- The hackathon centers on Fuel Blend Properties Prediction—participants develop AI/ML models to accurately predict properties of fuel blends, aiding sustainable and affordable energy solutions
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📅 Important Dates
Registration & Submission Deadline: ~July 20, 2025, based on social media countdowns .
Finals: Typically announced shortly after; expect live pitch rounds and finale details via HackerEarth.
🧩 Roles & Tracks
There are three participant tracks:
Student Edition
General Edition
Startup Edition
These cater to university students, professionals, and startup teams respectively .
🎯 Selection Process
Initial model submissions are evaluated automatically on validation data.
Top teams shortlisted based on accuracy/performance metrics.
Finalist stage involves live pitching to Shell judges and possibly industry experts (per past editions)
Education Required:
Open globally to:
Students
Individual professionals
Startups
No strict country restrictions; all are encouraged to build AI models for Shell
Any Graduation
Any stream
Fresher’s
Experienced also
skills
Participants should ideally be versed in:
Machine learning, particularly regression/prediction modeling.
Data science toolkits like Python, pandas, scikit-learn, TensorFlow, PyTorch.
Domain knowledge in chemistry or fuel blending is a strong asset.
Jupyter notebooks for model experimentation linkedin.com.
Experience with AI/ML model validation, performance tuning, and data preprocessing.
HOW TO APPLY :
• First, read through all of the job details on this page.
Scroll down and press the Apply Link button.
• To be redirected to the official website, click on the apply link.
• Fill the details with the information provided.
Before submitting the application, cross-check the information you’ve provided.
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SALARY :
Attractive full-time roles in energy analytics/data science — typically ₹10–30 LPA in India or $80–150K internationally (estimated based on industry norms).
