Machine Learning Expert

Multi-Layer Wireless Solutions

Description
The Multi-Layer Wireless Solutions (MLW) Research Area is active in the fields of multimedia and time-critical/broadband wireless communications: the two fields are respectively addressed by the MAIN (Multimedia Adaptation and Interaction), and BWA (Broadband Wireless Access) Research Units of MLW. MLW-MAIN activities concern the development of multimedia tools in a wide sense, including optimized encoders, automatic media processing and visual detection of events, visualization tools and multimedia applications (such as augmented reality). In these scopes we are looking for a candidate to research and implement novel machine learning and computational models to solve challenging real-world problems; the applicant will implement end-to-end experimentation, from algorithm design to extensive testing both offline and live.

Requirements and competences
• Computer Engineering or related field or Master’s Degree
• Expertise of at least one modern programming language such as Java, C/C++
• Expertise of at least one modern scripting language such as Matlab or Python
• Strong Computer Science fundamentals in data structures, algorithm design, machine learning, and complexity analysis
• 1+ years professional experience in software development
• Expertise of at least one of the following machine learning/deep learning frameworks: TensorFlow, Keras, Caffe, Torch, MxNet or Microsoft Computational Toolkit
• Understand project requirements and map them to scientific problems
• Good English, well spoken and written

Preferred qualifications
• Knowledge of image processing/computer vision algorithms and techniques
• Solid coding practices including good design documentation, unit testing, peer code reviews
• Experience building high-performance computational software
• PHD and experience abroad are a plus.

Other skills
• Teamwork
• Versatility and autonomy

Contract details
Type: fixed-term contract research full-time
Duration: 12 months
Location: ISMB (Turin).

Send your CV to the link http://bit.ly/2Fwh5uK.