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Position: Master Thesis „Detection and orientation estimation for pedestrians & cyclists via end-to-end learned representations”
Institution: Austrian Institute of Technology GmbH
Location: Wien, Austria
Duties: One task of this project is to elaborate various pose estimation concepts (orientation parameters from regression, near-infrared marker detection, skeleton-based fitting) using a key-point based multi-task learning framework; Moreover, you will investigate existing datasets to accumulate relevant training data; You will learn how to evaluate possible estimation techniques yielding 2D or 3D pose parameters; You will support our team in building a prototypic demonstrator system
Requirements: Ongoing master’s studies in the field of computer science, software engineering, information and computer engineering, mathematics, robotics or similar; Knowledge of fundamental Computer Vision and/or Machine Learning concepts; Knowledge of Python, preferably also PyTorch; Interest in turning exciting ideas into real Machine Learning based image analysis solutions in a challenging context; Good knowledge of verbal and written English
   
Text: We are Austria’s largest research and technology organisation and an international player in applied research for innovative infrastructure solutions. This makes us a powerful development partner for industry and a top employer in the scientific community. Our Center for Vision, Automation & Control in Vienna invites applications for a: Master Thesis „Detection and orientation estimation for pedestrians & cyclists via end-to-end learned representations” Assistive & Autonomous Systems Our team’s expertise is sensor and algorithm development for assistance systems of vehicles that recognize the environment in 3D. Assistance systems including artificial intelligence can help protect vulnerable road users such as pedestrians and cyclists. We cooperate with national and international research organizations and companies. One task of this project is to elaborate various pose estimation concepts (orientation parameters from regression, near-infrared marker detection, skeleton-based fitting) using a key-point based multi-task learning framework. Moreover, you will investigate existing datasets to accumulate relevant training data. You will learn how to evaluate possible estimation techniques yielding 2D or 3D pose parameters. You will support our team in building a prototypic demonstrator system. You will build multiple scenario-dependent training datasets for deep learning. You will develop a PyTorch-based algorithmic framework executing multiple detection & orientation/pose estimation schemes. You will develop an adapted python-based tracking scheme building on top of the functionalities of the algorithmic framework. You will evaluate and document test runs in pre-defined scenarios. Your qualifications as an Ingenious Partner* : Ongoing master’s studies in the field of computer science, software engineering, information and computer engineering, mathematics, robotics or similar Knowledge of fundamental Computer Vision and/or Machine Learning concepts Knowledge of Python, preferably also PyTorch Interest in turning exciting ideas into real Machine Learning based image analysis solutions in a challenging context Good knowledge of verbal and written English What to expect: EUR 779,25,- gross per month for 20 hours/week based on the collective agreement. There will be additional company benefits. You will be part of our international Young AIT network. As a research institution, we are familiar with the supervision and execution of master theses and we are looking forward to supporting you accordingly. At AIT, the promotion of women is important to us - that's why we are especially looking forward to applications from female students! Please submit your application documents including your CV, motivational letter and certificates online. Tomorrow Today - with You? Apply now! Apply online now Back to job listing Print × Close Interested? Drag your CV here or upload it to create a new profile. Upload your CV ;
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