|연구원||Post Doctoral Research Fellow in ICT - Machine Learning for Time Series Modelling|
|마감일||2019. 03. 31|
|기관명||[국외] University of Agder 바로가기 ☞|
Post Doctoral Research Fellow in ICT - Machine Learning for Time Series Modelling
University of Agder
Closing date: March 31, 2019
The University of Agder invites applications for a full-time, fixed-term appointment as a Post Doctoral Research Fellow in Information and Communication Technology for a period of two years. The position is currently located in Grimstad, Norway. The starting date is as soon as possible or to be negotiated with the Faculty.
The Department of ICT has three large and active research groups in Information and Communication Technology, including eight Professors, 20 Associate Professors/Assistant Professors and about 20 Research Fellows on the PhD programme in ICT. The Department of ICT pursues a variety of research interests, focusing especially on ICT and crisis management, artificial intelligence, eLearning and eHealth. The department has successfully led a number of large research projects funded by the Research Council of Norway, the EU research programmes FP7 and H2020 as well as national and international industries.
This open position is associated with the Centre for Artificial Intelligence Research
The aim of the postdoctoral research fellowship is to advance the state-of-the-art in machine learning techniques for time series modelling, classification and prediction. The work can either focus on fundamental issues such as novel learning algorithms and knowledge representation, or on applications such as health care, energy, safety and security, fraud detection, astronomy, human-computer interaction, bioconservation and others depending on the successful candidate’s interests and experience.
Research topics for the post doctoral research fellow include, but are not limited to the following:
• Machine learning methods (e.g. LSTM) for time series modelling
• Recurrent neural networks and reinforcement learning
• Application of machine learning methods to real-world time series
• Extraction of knowledge from trained neural networks
The ideal candidate will have expertise in the following areas:
• Recurrent neural networks
• Reinforcement learning
• One or more application areas of time series, modelling, classification and prediction
The successful applicant should hold a PhD in a related field (e.g. machine learning, artificial intelligence, data mining). To be considered for the position, applicants must have an approved PhD thesis by the closing date of the announcement.
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