Vacancy: PhD researcher Hybrid AI-based cognitive architecture for robotic applications

(07-10-2019)

Research Context

In recent times, deep learning has dominated the research landscape in Artificial Intelligence. Deep learning is a method of statistical (data-driven) learning that extracts features or attributes from raw data. Such networks and algorithms require a large number of labeled training examples and substantial computational resources, which are difficult to obtain in many complex real-world domains. In addition, the internal representations and reasoning methods of the learned models are rather difficult to interpret, whereas this explainability is a key requirement for their use in many application domains such as robotics, Industry 4.0, artificial control agents, etc.

An alternative strand of research in AI focuses on symbolic reasoning. A logic language is used to capture complex relationships and knowledge about the world. This information includes commonsense knowledge, and information about domain dynamics and context, obtained from humans or prior experience, or from interacting with the environment. Many logical formalisms have been developed to represent this knowledge using hierarchical relational structures and rules, and to provide explainable reasoning with such representations. However, these methods often require considerable human input and domain expertise to encode domain knowledge. In addition, it is challenging to use these methods to represent and reason efficiently with noisy sensor input, and to provide accurate descriptions of complex, realistic environments. Statistical methods like deep learning are better suited to capture this uncertainty.

IDLab’s research group on Distributed Machine Learning wants to investigate in this PhD how deep learning can be combined with logical-based formalisms in AI. The central research question is how to realize such an integration of hybrid AI technologies in robotic cognivite architectures.

Our offer

We offer a fully funded PhD scholarship. The PhD research is fundamental and innovative, but with clear practical applications. You will join IDLab’s Distributed Machine Learning group: a young and enthusiastic team of researchers, post-docs and professors. The PhD position is immediately available.

Besides conducting fundamental research, you will contribute to educational activities in the Master of Science in Information Engineering Technology. You will, together with colleagues, prepare assignments and guide students during PC lab sessions.

Requirements

  • You have a degree in Master of Science/Engineering, preferably in Computer Science, or (Mathematical) Informatics. You must have a solid academic track record (at least graduation cum laude). Please do not hesitate to contact us regarding these administrative matters.
  • You have a strong interest in one or more of the following domains: deep learning, robotics, reasoning, planning, logical formalisms. Proven experience in one of these domains, e.g. via your master thesis or course projects, is a plus.
  • You have profound programming skills in Python or Java, additional knowledge of C/C++ is a plus.
  • You are interested in and motivated by the research topic and the use case, as well as in obtaining a PhD degree.
  • You are willing to help assisting courses of the Master of Information Engineering Technology
  • Knowledge of Dutch is a plus. You speak and write English fluently.
  • You have good communication skills and you are a team player.
  • You have an open mind and a multi-disciplinary attitude.

Interested?

Apply with motivation letter, scientific resume, abstract of your master thesis, diplomas and detailed academic results (courses and grades), relevant publications, and at least one reference contact. This information, as well as possible questions, must be sent to Prof. Pieter Simoens (pieter.simoens@ugent.be)

After the first screening, suitable candidates will be invited for an interview (also possible via Skype) and may get a small assignment. Applications will be screened as soon as they are received. The position is open until the vacancy is filled.

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