INTRODUCTION

AUA in partnership with PicsArt are thrilled to announce the launch of a new Artificial Intelligence paid training and research program for current students and recent alumni of Computational, Mathematical and Science programs.

WHY BECOME JUNIOR RESEARCHER?

This is a rare opportunity for Computer Science and Data Science students to: 

  • Explore immense opportunities in research, learn how to experiment with cutting edge tools and technologies, 
  • Receive advanced and tailored training and mentorship from local and international faculty and industry experts, 
  • Apply knowledge to real big data sets, talk to real problem owners, have an impact on decisions of international companies,
  • Get Paid for work that also enriches, deepens and accelerates your learning experience at the university
  • And last but not least, most of the companies involved in cool and creative R&D projects always give priority to candidates with research experience
COMPENSATION

Based on experience, knowledge and progression with the training, junior researchers will be offered compensation.

WORK/STUDY BALANCE

Junior researcher positions are part-time positions and the work will be scheduled not to interfere with your classes.

EXPOSURE

The selected team of junior researchers will work in a dedicated AI Lab co-founded by AUA and PicsArt and will get an opportunity to learn from and work with AI professionals and professors from Armenia and best universities and various companies worldwide.

IMPORTANT

REQUIREMENTS AND PREREQUISITES FOR BOTH TRACKS

MACHINE LEARNING

Prerequisite for Machine Learning:

  • BS, MS degree or student of Computer Science, Statistics, Applied Mathematics, Machine Learning or related technical fields
  • Background in statistics and Linear Algebra
  • Basic knowledge of Python coding
  • Basic knowledge of SQL is a plus

Classic Machine Learning

  • Supervised Techniques (Logistic Regression, Decision Tree, SVM, KNN etc)
  • Unsupervised Techniques (PCA, K-means, DBSCAN etc)

Deep Learning

  • Supervised Techniques (ANN, CNN, RNN)
  • Unsupervised Techniques (AutoEncoder, GAN)

Python

  • Object-Oriented Programming
  • Main Libraries (NumPy, SciPy, Pandas, Matplotlib, Seaborn, Sklearn)
  • Deep Learning Frameworks (Tensorflow, Pytorch)

Data Mining

  • SQL
  • Spark
  • Exploratory Data Analysis
  • Feature Engineering

 

Application Domains for gained knowledge and experience

/Predictive Modeling / Recommendations Systems / User Behavior Research / NLP based solutions / Image Domain Transfer / Image to Image Translation / Image Classification / Image Recognition / Object Detection

COMPUTER VISION

Prerequisite for Computer Vision:

  • BS, MS degree or student of Computer Science, Statistics, Applied Mathematics, Machine Learning or related technical fields
  • Background in Statistics, Linear Algebra and Differential Equations
  • Basic knowledge of C++ coding

Image Processing

  • Image Filtering (Blurring, Sharpening, Sepia, etc)
  • Image Blending (Poisson, Regular)
  • Image Statistics (Histogram Matching, Equalization, etc)
  • Image Spatial Transformation (Affine, Perspective, Warps, etc)
  • Signal Processing basics

Computer Vision

  • Image Feature Detection(SIFT, SURF, HOG, etc)
  • Object Recognition and Detection (Face detection, Pose Estimation , etc)
  • Motion Analysis (Tracking, Optical Flow, etc)
  • Image Reconstruction (Noise Removal, Inpainting, etc)
  • Optical Character Recognition
  • C++

 

  • Object-Oriented Programming
  • Data Structures
  • Design Patterns
  • Main Libraries (Boost, OpenCV, Tesseract, Dlib, etc)

 

Application Domains for gained knowledge and experience

Image processing / Computer Vision /

C++ Development