Vacancies

I have a PhD position in Large Language Models for Recommendation, funded by the NorwAI research-based innovation center.

The proposed PhD project aims to advance the field of personalized recommender systems by harnessing the natural language reasoning capabilities of large language models (LLMs). The research will focus on three key areas:

1. developing methods to construct natural language user interest profiles that enhance transparency and provide user control over recommendations; 
2. designing conversational recommendation systems that utilize LLMs to effectively elicit user preferences and generate tailored responses, including both recommendations and explanations; and
3. developing approaches to mitigate limitations of LLMs through retrieval-augmented generation (RAG) and tool use.

Overall, this project seeks to push the boundaries of how LLMs can be applied to create more intuitive, responsive, and user-centered recommendation systems.

See the details on jobbnorge. Application deadline is Oct 31.

We have a PhD position in language modeling for explainable AI, funded by SFI NorwAI, the new 8-year research-based innovation Center for AI Innovation.

We are looking for applicants with a strong academic background who have completed a five-year master degree (3+2) within Computer Science, more specifically within information retrieval or natural language processing, preferably acquired recently; or possess corresponding qualifications that could provide a basis for successfully completing a doctorate.

Details and application at Jobbnorge. Deadline: October 17, 2021.

We have a PhD position, funded by SFI NorwAI, the new 8-year research-based innovation Center for AI Innovation.

AI-powered systems exhibit a growing degree of personalization when recommending content. Users, however, have little knowledge of how these systems work and what personal data they use. There is a need for transparency, in terms of (1) collecting and using personal data, and how it is used for inferring user preferences and (2) explaining and justifying the generated recommendations. The most human-like form of providing explanations is via natural language. This would allow users to better understand how their preferences are understood and interpreted by the system, and also correct it if necessary.

The main goal of the project is to develop novel models that enable semantically rich and context-dependent text-based explainability of user preferences and system recommendations, using either existing metadata or automatically extracted annotations. A starting point for generating explanations is template-based; later, this can be made more human-like using language generation techniques, using large-scale (pre-trained) language models.

Visit Jobbnorge for more information and application details (notice the requirement for a cover letter).
Application deadline: March 15 June 1.

I have a fully-funded PhD position in Conversational AI. This position is partially funded by an unrestricted gift from Google, and will be co-supervised by Google research scientist Filip Radlinski.

Conversational search is a newly emerging research area within AI that aims to provide access to digitally stored information by means of a conversational user interface. The goal of such systems is to effectively handle a wide range of requests expressed in natural language, with rich user-system dialogue as a crucial component for understanding the user’s intent and refining the answers.

The overall objective of this project is to develop a prototype conversational search system for supporting scholarly activities (Scholarly Conversational Assistant). Scholarly activities of interest include, among others, finding relevant research material, planning conference attendance, or finding relevant experts to serve as speakers, committee members, etc. You can find further details on the envisioned functionality of the Scholarly Conversational Assistant here.

Specific areas of functionality targeted in the project concern the modeling of user knowledge, adapting the assistant’s language usage accordingly, and system-initiated (proactive) notifications.

Visit Jobbnorge for more information and application details (notice the requirement for a cover letter).
Application deadline: Nov 26.

The University of Stavanger invites applications for a fully funded PhD position.

Knowledge graphs, organizing structured information about entities, and their attributes and relationships, are ubiquitous today. They have become powerful assets for a broad range of search, recommendation, and mining scenarios. Examples include enabling rich knowledge panels and direct answers in search result pages, supporting data exploration and visualization, and facilitating media monitoring and reputation management. This project focuses on the usage of knowledge graphs for conversational AI, in particular, for conversational search and recommendation tasks.

The recent success of deep learning techniques in different areas of natural language processing has enabled conversational AI systems to generate human-like responses. These systems, however, still have from little to no understanding of the actual meaning of the dialog. Knowledge graphs are needed to make human-machine interactions more grounded in knowledge. Knowledge graphs may also be utilized for personalized experiences.

This project involves various tasks around knowledge graphs for conversational information access, including the development of (i) models of interaction, (ii) algorithms for personalized search and recommendation, (iii) methods for multi-modal result presentation, and (iv) evaluation methodology of resources.

The candidate is expected to have a background in information retrieval, natural language processing, or machine learning.

See this page for the application instructions. Note that you’ll need to provide a cover letter expressing your interest in this specific project.

Application deadline: January 12, 2020.

This position has been re-announced. Application deadline: April 19, 2020.