ATIS has started an internship program in Machine Learning/Computer Vision.
Computer Vision is a discipline of computer science that consists of methods for acquiring information of the surrounding environment by analyzing and processing images or videos. One fundamental task in computer vision is visual recognition: ability of a software to analyze the content of images and/or videos and identify objects, people, places and actions
Motivation & Challenges
Object recognition finds numerous applications such as in security (e.g. scene surveillance and monitoring), face recognition, augmented reality, medical visual data analysis, smart photo libraries and automatic photo tagging, automatic ecommerce product categorization, autonomous driving (car & pedestrian detection), content moderation, optical character recognition, manufacturing quality control, targeted advertising and many more.
Visual recognition, although comes natural for humans, it is a very challenging task for computers. This is because the scene that is captured by a camera can vary a lot in appearance due to changes in illumination, viewpoint, intra-class appearance, clutter, camera noise etc.
Recently, artificially intelligent systems i.e. deep learningbased ones, have proved to be very successful for visual recognition tasks and Convolutional Neural Networkshave become the standard for building such algorithms. The goal of the internship program, is to build 3 deep learning based visual recognition systems which will focus respectively in the following 3 applications:
- Web content moderation for images and videos: automatically filter or flag unwanted media from user-generated content
- E-commerce visual product search and recommendation: allow customers to search for visually similar products
- Automatic categorization of large media databases: organize collections of media through auto-tagging
Program: The internship program work will be divided in 5 steps:
- First the interns should get familiar with classical Image Processing and Computer Vision. During this period, they should practice computer vision tasks with Python and PHP programming.
- Then the interns should get familiar with basic Machine Learning and a little bit more modern Computer Vision techniques based on Deep Learning and in particular Convolutional Neural Networks.
- After that all interns should get familiar with the Deep Learning framework of CAFFE (https://caffe.berkeleyvision.org) & Tensorflow (released from Google recently) which is becoming the standard framework for academia and industry.
- Later the interns will become more active in training and testing deep learning algorithms for object recognition. In this phase they will work as a group together.
- In the final stage the interns will start their individual projects following the one the 3 applications mentioned before: content moderation, media categorization end ecommerce visual search.
- Basic knowledge of Python. Good knowledge of PHP. C++ and/or Matlab is considered a plus
- Motivation to invest time and learn cutting edge technologies within a very exciting field of computer science
- Bachelor’s degree in Computer Science, Mathematics, Engineering or a related field
- English is a must
The interns will be mentored by Dr. Thoma Papadhimitri, computer vision expert, co-founder of ATIS sh.p.k – www.atis.al
Short Bio: Graduated from Bologna University in Italy in 2009 (with highest honors) in Electronic Engineering, he later obtained a PhD in 2014 in computer science from Bern University in Switzerland. During this time, he published at top conferences and journals of Computer Vision (e.g. CVPR, IJCV), an important topic of today’s computer science. In 2014 he joined Huawei in Munich as a Research Engineer where he worked on artificial intelligence and computer vision till 2017. He also holds an international patent.
Intellectual Property Rights
At the end of the internship program all intellectual property e.g. code, product, inventions etc will remain of ATIS property.
Please send an email with CV at: firstname.lastname@example.org
Only shortlisted candidates will be contacted for an interview.