Why ADAPT?

• Contribute to the ADAPT research agenda that pioneers and combines research in AI driven technologies: Robotics, Natural Language Processing, Video/Text/Image/Speech processing, digital engagement & HCI, semantic modeling, personalisation, privacy & data governance. 
• Work with our interdisciplinary team of  leading experts from the complementary fields of, Social Sciences,  Communications, Commerce/Fintech, Ethics, Law,  Health, Environment and Sustainability.
• Leverage our success.  ADAPT’s researchers have signed 43 collaborative research projects, 52 licence agreements and oversee 16 active commercialisation funds and 52 commercialisation awards.  ADAPT has won 40 competitive EU research projects and obtained €18.5 million in non-exchequer non-commercial funding. Additionally, six spinout companies have been formed. ADAPT’s researchers have produced over 1,500 journal and conference publications and nearly 100 PhD students have been trained. 

As an ADAPT funded PhD researcher you will have access to a network of 85 global experts and over 250 staff as well as a wide multi-disciplinary ecosystem across 8 leading Irish universities. We can influence and inform your work, share our networks and collaborate with you to increase your impact, and accelerate your career opportunities. Specifically we offer: 

1. Opportunity to build your profile at international conferences and global events.
2. A solid career pathway through formalised training & development, expert one-on-one supervision and exposure to top specialists.
3. A Fully funded, 4 year PhD postgraduate studentship which includes  a stipend of (€18,500 per annum – non taxed), along with equipment, annual travel funding 
4. Funding for annual student fees 

Context

Next generation service robots offer the potential to provide efficient, flexible, and reliable performance across a range of environments that may be occupied with human users. This introduces challenges around enhancing the autonomous performance of the robot, such that it can monitor and model multiple users, making its behaviour more predictable and understandable to nearby observers. The performance of the robot must be robust to a range of different use-contexts and users, ranging from co-workers trained to use the robot to members of the public. Research in human-robot interaction, situated conversational systems, and even in the automotive space has tended to focus exclusively on a direct user engaged in a task with the system, or on safety critical issues such as avoiding collisions. The middle ground of users that might complete tasks alongside service robots, or even users that may simply need to engage the robot during its own activities have been largely unexplored. 

This PhD will look at the issue of user model construction and constructive task-oriented feedback in service robotics. The focus will be on real world deployments where the novelty factor will be minimized — thus leading to richer real-world insights into the human-robot relationship. Rather than having to build a robot from scratch or rely on robots not capable of completing real world tasks, this work will where possible take advantage of existing robot platforms to address the specific challenges around modelling of multiple users in collaborative environments, and the benefits of merging the conversational modality with non-conversational communication channels. As such, the goal here is not to study interaction from a robot as human perspective, but instead from a robot perspective where the conversational and gestural modalities are just one form of interaction. This research will take a data driven approach with a focus on situated interaction. A work plan involving ethnographic studies, software and systems development, and end user studies is envisaged. 

The position is best suited to a computer engineering or computer science student (or related) with a background in data science and NLP and with some experience of programming robots, preferably using the robot operating system (ROS). 

Minimum qualifications

• Bachelor’s Degree in Computer Engineering, Computer Science (or related field) with training in Natural Language Processing and or Deep Learning 

Preferred qualifications

• Master’s Degree in Machine Learning, Data Analytics, Human-Computer Interaction or related field

Application Process

Each application should only consist of

1. Detailed curriculum vitae, including – if applicable – relevant publications;
2. Transcripts of degrees,
3. The name and email contacts of two academic referees,
4. A cover letter/letter of introduction (max 600 words). In the letter, applicants should include the following details:
   1. An explanation of your interest in the research to be conducted and why you believe they are suitable for the position.
   2. Details of your final year undergraduate project (if applicable)
   3. Details of your MSc project (if applicable)
   4. Details of any relevant modules previously taken, at undergraduate and/or Master level.
   5. Details of any relevant work experience (if applicable).

Informal inquiries can be directed to Robert Ross via email [robert.ross@tudublin.ie]