PhD Scholarship in Machine Learning Based Perception for Inspection

Technical University of Denmark, Denmark is inviting excellent students to apply for a PhD position in acoustics. Applications are invited up to July 26, 2019.

The Automation and Control Group at the Department of Electrical Engineeringinvites applicants for 3-years PhD position in the area of Machine Learning-based Perception for Inspection.

The goal of this PhD project is to design, implement and test novel perception and machine learning methods for inspection in the maritime industry. The project will require understanding the application domain which includes confined and highly reflective/dark areas designing methods for the detection and classification of faults in vessel surfaces using Machine Learning (including but not limited to Deep Learning), producing perception to assist the navigation of an aerial vehicle, testing and validating the developed technologies in real-life applications.

The selected candidate will have the opportunity to work closely with major players in the Danish maritime industry in the field of aerial inspection and will be part of a vibrant team with strong academic competences at DTU, where the position will be based. Cooperation with the other members of the project team and equal contribution to common building blocks of the developed system is foreseen. Assistant Professor Evangelos Boukas will supervise the selected student.

(AUT) at DTU Department of Electrical Engineeringperforms research in the technical sciences related to automation. A holistic and interdisciplinary approach is applied to the research within the field of robust autonomous systems and it is pursued as the key to the development of automation solutions able to reliably perform under uncertainty. The group research activities branch into three major directions: condition monitoring and decision support for safety critical systems; reconfigurable and self-learning systems; perception and situation awareness under uncertainty. AUT also holds strong expertise in perception for mobile autonomous robots through integration of smart sensor technologies, probabilistic methods and machine learning techniques. The available expertise in those areas is also outsourced for public and private consultancy. The selected candidates will be responsible of the design and implementation of a machine learning -based and 3D-based perception system for the detection of faults in structureswithin the maritime industry. Expected contribution: The research the selected candidate is expected to carry on could include dynamic modelling of aerial manipulators, control of physical interaction of floating-base systems, design of innovative mechanisms for aerial manipulation, AI for aerial manipulation, and the like.

Applicants must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree. Candidates may apply prior to obtaining their master’s degree but cannot begin before having received it.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.

DTU Electrical Engineering educates students within electrical engineering technologies. We offer studies at BEng, BSc, MSc and PhD levels, and participate in joint international programmes. We conduct state-of-the-art research within antenna and microwave technology, robot technology, power and physical electronics, acoustic environment, electro-acoustics, electric power and energy. Our department has more than 200 members of staff. DTU is a technical university providing internationally leading research, education, innovation andscientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent university collaborating globally with business, industry, government and public agencies.

Deadline: July 26, 2019
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