Make IN-D Smarter – Join the AI Lab at Intain – Internship + Job Offer
December 22, 2018
|3 Steps to the AI Lab at Intain|
|Announcement (in 10 select colleges in southern India)||Wednesday, January , 2nd|
|Last Date to Register||Friday, January 11th|
|Step 1 – Assessment (Aptitude+Algorithm) -2 slots||Saturday, January 12th|
|Step 2- Hackathon (for Selected Candidates)||Saturday, January 19th|
|Step 3- Final Interviews (In person or Google Hangout)||January 26th to February 2nd|
|Offers||Monday, February 4th|
|What you get|
|Runner Up (2 Awards)||Rs. 25,000 each|
|Finalists||Rs. 10,000 each|
Internships – Up to 6 internships for the final semester students, Rs. 25,000p.m. (M.Tech) and 20,000 (B.Tech)
in Computer Science or Electronics Engineering
Employment – All selected interns would join Intain on completion of their course at 7.5L p.a. for B.Tech and 9L
p.a. for M.Tech. In case of exceptional candidates, there would an additional bonus component.
Mentorship – Judges and Mentors would be accomplished researchers in the field of Artificial Intelligence who
would be available for interaction through this process and beyond
- Someone young and enthusiastic who wants to work on developing new products, is keen to learn and willing to
live on the edges of his technology comfort zone
- B.Tech/BE or M.Tech/ME in Computer Science or Electronics Engineering or Masters in Mathematics completing
his course in 2019 and eligible for a 4-6 months internship
- Strong mathematical ability and high level programming skills in Python
- Our team works across following areas – skills backed by project work in
any of the following areas with a big plus :
- Image Processing: Morphological Processing, Segmentation, Object Identification and Localization.
- Should be good in linear algebra, matrix, if worked on OCR system will be a plus.
- Feature Engineering: Feature Extraction, Feature Selection, Sample Estimation, Principal Component Analysis,
Linear Discriminant Analysis, etc.
- Classification: SVM, ANN, Bayes, Decision Tree, Random Forest, etc.
- Clustering: K-Means, Hierarchical Clustering, Density-based spatial clustering of applications with noise
- Model Selection: Precision/Recall/F1 Score, K-Fold Cross Validation, Overfitting, Underfitting, etc.
Natural Language Processing:
- Named Entity Recognition, Information Retrieval, Document Classification, Query Parsers, Language Modelling,
- Advance Machine Learning: Convolution Neural Networks, Recurrent Neural Network, Long-Short Term Memory,
Application of these techniques with respect to computer vision and natural language processing
- Technical Skills: Python, python libraries: numpy, pandas, sklearn, tensorflow, NLTK, etc.textblob, genism,
- Knowledge of the OCR/ICR system would be recommended
If you have any queries, write to firstname.lastname@example.org