Application Form
PhD Studentship in Machine Learning &Neurolinguistics (PhD_MLN_GDL)

JobRef: 9835
  • Accepted file types: pdf.
  • Accepted file types: pdf.

PhD Studentship in Machine Learning &Neurolinguistics (PhD_MLN_GDL)

Level:PhD
POSTED:July 22, 2021
LOCATION:Trinity College Dublin
CLOSES:August 10, 2021
Duration:4 Years
Reports to:Prof Giovanni Di Liberto
Salary:Annual €18,500 (non taxed) plus university fees
Closing Date:August 10, 2021

Why ADAPT?

  • Contribute to the ADAPT research agenda that pioneers and combines research in AI driven technologies: 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

The ability to process large datasets has allowed for the rapid advance of speech and natural language processing technologies. Despite a series of recent breakthroughs and success in performing specialised tasks, the field of AI has yet to produce algorithms that can truly understand language as humans do. One solution is to study the most sophisticated “device” that we know of: The human brain.

The present project aims to better understand how our brains transform speech sounds into meaning by studying the brain encoding of speech with machine learning methodologies. The results of this project are expected to have a strong impact across disciplines. The findings on speech processing will contribute to the area of neurolinguistics advancing our understanding of speech processing in health, development, and disease, potentially leading to novel tools for early diagnosis of language-related deficits. Indeed, the novel computer science methodologies for neural data analysis developed during this project will impact the field of cognitive science at large. Finally, the findings may also translate into innovative AI solutions for speech and language communication.

Upon completion of the work, the successful candidate will be an expert in neural data science, speech and language processing, and neurolinguistics. As such, they will be in the position to use AI methodologies to answer cognitive science questions and, vice versa, to use knowledge from neurolinguistics to advance AI solutions. They will be competitive candidates for both academic and industry research positions as well as for high-profile data science roles. Their new expertise in analysing challenging neural data will put them in a position to work in areas involving data with similar complexity, as well as to work in areas such as neural-engineering. 

The student will be supervised by Prof Giovanni Di Liberto (TCD; lab website: diliberg.net). The successful candidate will also have the opportunity to interact with experts from different relevant disciplines across the ADAPT institutions and the Trinity Centre Institute of Neuroscience. 

Minimum qualifications

  • First class honours undergraduate degree in Computer Science, Electronic/Computer Engineering or similar discipline
  • Skills: programming, signal processing and/or machine learning

Preferred qualifications

  • MSc in Computer Science, Electronic/Computer Engineering, Cognitive Science or similar discipline
  • Skills: neural data recording and analysis, speech processing, NLP, linguistics

Informal inquiries should be directed to Prof Giovanni Di Liberto (diliberg@tcd.ie).

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 800 words). In the letter, applicants should include the following details:
    • An explanation of your interest in the research to be conducted and why you believe they are suitable for the position. Please mention if you have particular relevant research questions that you would like to pursue.
    • Details of your final year undergraduate project (if applicable)
    • Details of your MSc project (if applicable)
    • Details of any relevant modules previously taken, at undergraduate and/or Master level.
    • Details of any relevant work experience (if applicable).

Diversity

ADAPT is committed to achieving better diversity and gender representation at all levels of the organisation, across leadership, academic, operations, research staff and studentship levels. ADAPT is committed to the continued development of employment policies, procedures and practices that promote gender equality. On that basis we encourage and welcome talented people from all backgrounds to join ADAPT.


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