University of Stavanger in Norway invites applications for fully funded PhD positions in Deep Learning at Department of Computer Science and Electrical Engineering.
Two projects related to Deep Learning are:
1. Deep network representations for scalable graph mining
Recently application of network embeddings has been increasingly popular for graph mining tasks such as community detection, link prediction, node classification, link classification and temporal network analysis. Techniques such as DeepWalk and Node2Vec have been proposed to learn network features vectors to solve these tasks in an unsupervised and generic manner. While these techniques are simple, their genericness renders them ineffective for specific tasks. These existing representations are learned by exploring the graphs locally through random walks and hence are not deep. Most social networks and web graphs on the other hand are known to have hierarchical structures. Learning appropriate representations to capture these hierarchical structures remains to be a challenge. Finally, the existing techniques are computationally expensive and are limited to small and medium sized graphs. Motivated by the above issues, in this project we seek to solve three main research questions: (1) Using a generic framework can we learn task-specific representations? (2) Can we learn richer representations to capture the hierarchical structures of the networks using recent advances in deep learning rather than simple random walks in the networks? (3) Can we scale the learning process using techniques such as distribution preserving sampling and asynchronous training? This thesis will address these research questions and propose a novel framework for learning task-specific, deep network embeddings for large-scale graphs.
The candidate is required to have a strong background in machine learning or deep learning and graph mining.
Supervisors: Associate Professor Vinay Setty UiS, email@example.com and Junior Professor Avishek Anand firstname.lastname@example.org (L3S and Leibniz Universität Hannover Germany)
2. Conversational AI for information access and retrieval
Intelligent personal assistants and chatbots (such as Siri, Cortana, the Google Assistant, and Amazon Alexa) are being used increasingly more for different purposes, including information access and retrieval. These conversational agents differ from traditional search engines in several important ways. They enable more naturalistic human-like interactions, where search becomes a dialog between the user and the machine. Unlike in traditional search engines, where a user-issued query is answered with a search result page, conversational agents can respond in a variety of ways, for example, asking questions back to the user for clarification.
The successful candidate will work on the design, development, and evaluation of conversational search systems. In particular, the candidate is expected to employ and develop deep learning techniques for understanding natural language requests and generating appropriate responses. The candidate is required to have a background in machine learning or information retrieval.
Supervisor: Professor Krisztian Balog, email@example.com
What we offer in a nutshell:
1. A strong research environment with supervision from experienced faculty.
2. Opportunities to collaborate and for research stays with our renowned collaborators worldwide, especially in Germany, Denmark and Netherlands.
3. Well-paid PhD position, in a country which has been ranked by the UN as having the highest standard of living in the world, which is known for its unique scenic beauty. Norway was also named as the world’s happiest country recently.
4. We use English for research and communication. Although opportunity to learn Norwegian language will be provided free of cost.
Suitable Background and Requirements:
1. Applicants must have a Masters degree in Computer Science, or in a related study, with excellent grades. They must also be able to demonstrate interest in scientific research. The evaluation considers many aspects of excellence, as well as the personal drive and organizational skills. The ideal candidate for the position will have strong background in distributed computing.
2. You may apply if you have not yet completed your degree, but expect to do so before the position starts.
3. Experience or publications related to any of the following areas is a bonus: graph mining, machine learning, deep learning, text mining and Information Retrieval.
For more information, please contact Vinay Setty (firstname.lastname@example.org), Krisztian Balog, (email@example.com) or Tom Ryen (firstname.lastname@example.org)
For detailed information about the PhD position and the application process, please see: https://www.jobbnorge.no/en/available-jobs/job/147261/research-fellow-in-computer-science-signal-processing-or-cybernetics
Please remember to specify topic "2. Deep network representations for scalable graph mining" and "7. Conversational AI for information access and retrieval" as your top two preferences. You can specify up to three topics of your choice.
Deadline for the application: Tuesday, February 27, 2018