Overview

University of Stavanger in Norway invites applications for a fully funded PhD position in “Conversational AI” or “Graph embeddings” at Department of Computer Science and Electrical Engineering.

1. 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
Co-Supervisor: Professor Vinay Setty, Email

2. Distributed Deep learning for massive-scale graphs

Graphs are ubiquitous data-structures and are used to represent social networks, knowledge graphs and biological networks. Traditionally many graph structural features are used for graph tasks such as community detection, link prediction, node classification, label propagation and recommendation systems. Recently application of network embeddings in the place of simple graph features has been increasingly popular for such graph mining. 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. Even though these techniques are promising for some applications, they have two major limitations: (1) they are based on shallow networks to learn neighborhood features, (2) they cannot scale beyond 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.

To address these limitations, recent concept of Graph Convolutional Networks (GCNs) were proposed. However, scaling them to massive graphs requires distributed setting. This project sets design distributed deep learning technique to distribute and parallelize model training across several GPUs. There are challenges regarding partitioning the graphs and the aggregation of local models are aggregated using parallel paradigms in an efficient way.

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
Co-Supervisors: Professor Krisztian Balog, Email and Avishek Anand, Email

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 worldäs happiest country recently.
4. We use English for research and communication. Although opportunity to learn Norwegian language will be provided free of cost.

Company:

University of Stavanger

Qualifications:

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 machine learning and deep learning.
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.

Educational level:

Master Degree

How to apply:

Please mention NLP People as a source when applying

https://www.jobbnorge.no/en/available-jobs/job/156968/research-fellow-in-computer-science-signal-processing-or-cybernetics

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About Universitetet i Stavanger

The University of Stavanger, Norway, has about 9000 students and 1200 administration, faculty and service staff. The University is organized in three Faculties and also includes two national centres of expertise.

The Faculty of Arts and Education has about 2500 students.
The Faculty represents something quite novel among Norwegian Universities. For the first time vocational programmes such as the general teacher training and the pre-school teacher training programmes have been incorporated into a university.

The Faculty of Social Sciences has about 3400 students and is the largest at the University of Stavanger.
The Faculty has a range of courses that are popular and in high demand among students. The combination of academic disciplines and professional training programmes in the areas of social sciences, economics, health and social studies appeals to many students.

The Faculty of Science and Technology has about 1700 students, and is divided in five departments.
The Faculty is in some areas