[iva] Fully Funded PhD position in machine learning for conversational AI, KTH Royal Institute of Technology

[Joakim Gustafsson]

The School of Electrical Engineering and Computer Science (EECS) at the KTH Royal Institute of Technology announces a fully funded Ph.D position in machine learning for conversational AI at the division of Speech, Music, and Hearing (TMH).



ABOUT KTH

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key center of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as in architecture, industrial management, urban planning, history and philosophy.

The doctoral position is offered by the division of Speech, Music and Hearing (http://www.kth.se/tmh). It is an internationally prominent research group within speech technology and related areas with a heritage that ranges back to the early 50s. It was one of the first research groups of its kind, and is likely the oldest that is still active. The group is in the forefront of fundamental research in areas such as speech analysis, analysis of human communicative behaviours, audiovisual synthesis and generation, speech perception and understanding, and multimodal dialogue systems.


PROJECT DESCRIPTION

Within the VR-funded project “Connected - Context-aware speech synthesis for conversational AI” we look for one fully funded Doctoral student. In this project the aim is to develop and evaluate a context-aware conversational speech synthesizer that allows for implicit control over the manner of speaking to cater for the communicative needs of different interactive scenarios. More specifically, we will develop different strategies to adapt the synthetic voice to changes in the communicative setting and in response to the verbal and non-verbal behavior of the conversational partner. Some recent papers and associated TTS samples can be found at: https://www.speech.kth.se/tts-demos/ and https://tinyurl.com/15cljys3



Supervision: The doctoral student will be supervised by Joakim Gustafson and  Éva Székely



The starting date for the positions is flexible, but as soon as possible.



What we offer

- Workplace that focus on working conditions, gender equality, diversity, study environment

- A monthly salary of around 3.000 EUR, and benefits such as 6 weeks paid vacation, health insurance, paid parental leave and occupational pensions.



QUALIFICATIONS

The candidate must have a degree in Computer Science or related fields.  In order to succeed as doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to work both independently and to collaborate with others. Other important traits are openness toward interdisciplinary research, creativity and a structured approach. Good command of spoken and written English is required. Expertise in machine learning/deep learning and good programming skills are strongly desirable. Experience and interest in areas like speech technology, signal processing, natural language processing, linguistics, perception is also considered as asset. After the qualification requirements, great emphasis will be placed on personal qualities and personal suitability.



HOW TO APPLY



The application should include:

1. Curriculum vitae.

2. Transcripts from University/College.

3. Brief description of why the applicant wishes to become a doctoral student.



The application documents should be uploaded using the KTH's recruitment system. More information here:

https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:379667/where:4/



The application deadline is ** April 2, 2021 **



-----------------
Joakim Gustafson
Professor, Head of Division, Co-head of Department
KTH Royal Institute of Technology
School of Electrical Engineering and Computer Science
Department of Intelligent Systems

Division of Speech, Music and Hearing (TMH)


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