PhD Candidate: Speech Processing for Diagnosing Neurodegenerative Disease

  • WO
  • Tijdelijk
Radboud Universiteit


Are you eager to investigate new algorithms for the diagnosis of disease by voice?

AI-based voice diagnostics analyse spoken audio using machine learning methods. Responsible application of AI-based voice diagnostics holds great promise in healthcare because of its usefulness in diagnosing and monitoring disease. This project develops AI-based voice diagnostics for the most common neurodegenerative diseases, Parkinson's and Alzheimer's, using speech analysis. This project closely collaborates with medical experts in the area of neurodegenerative disease to understand how to responsibly collect data necessary to train AI-based voice diagnostic models. It also addresses the challenge of fine-tuning foundation models for classification under limited data conditions. Finally, it tackles a key shortcoming of current models trained for the detection of neurodegenerative disease: the lack of explainability of these models makes it difficult to determine the reasons behind correct and incorrect predictions.

The project will leverage recent ideas from data-centric AI, explainable AI and data minimisation. As a PhD candidate, you will make a creative contribution in putting these ideas to work for supporting the protection of spoken audio recordings in the setting of medical diagnosis.  You will work in a multidisciplinary group consisting of PhD candidates, postdoctoral researchers and professors in different domains such as linguistics, language and speech technology, speech recognition (ASR), NLP, data privacy and responsible AI.

You will be responsible for 1) creating speech data collection specifications and risk assessment in collaboration with stakeholders from the medical domains, 2) developing and testing algorithms that are able to diagnose disease on the basis of speech data, making use of the principles of responsible AI, and 3) designing and validating novel techniques for explaining the results of algorithms, making them more useful in a clinical setting. You will be expected to publish your results in leading conferences and journals. The position may also include other duties such as supervision of Bachelor’s and Master’s students.


  • You have a Master’s degree in computer science, electrical engineering, machine learning, speech and language technology, or equivalent.
  • You have proven expertise in one or more relevant domains including speech processing and speech recognition.
  • You have proven expertise or a high interest in responsible AI.
  • You have an interest in data-centric AI and/or previous experience in dataset development (data preparation), especially in the domain of speech and/or health.
  • You have a demonstrable affinity for academic research as evidenced by publications and/or presentations.
  • You have an interest in interdisciplinary research and are able to communicate across domain boundaries.
  • You are able to work independently within the larger context of an interdisciplinary team.
  • You have Python and UNIX skills.
  • You have some experience with using foundational models for speech and language.
  • You are fluent in spoken and written English.


Working on the NWO-funded research project ’Responsible AI for Voice Diagnostics’ led by Dr Cristian Tejedor-García, you will be part of a new and proactive group of six PhD candidates and several postdoctoral researchers and professors at the Centre for Language Studies (CLS). The project leverages Radboud AI, Radboud University's campus-overarching, interdisciplinary AI initiative which connects the faculties of Arts,  Science and Social Sciences with the Radboud university medical center. The project has links with the Radboud Healthy Data programme, the National AI Education Lab (NOLAI), the AI Hub East Netherlands, relevant health-related Innovation Centres for Artificial Intelligence (ICAI) labs, and the European Laboratory for Learning and Intelligent Systems (ELLIS). The project addresses the challenges related to machine learning, natural language processing, data dependencies, quality and enrichment, ethical dimensions of AI and regulatory requirements for AI.

The Faculty of Arts is committed to knowledge production with a significant scientific and social impact. With over 500 academic and support staff, we teach and conduct research in the fields of history and art, languages and cultures, and linguistics and communication, using innovative methodologies and working in close collaboration between the disciplines. Our research is embedded in two research institutes: CLS and the Radboud Institute for Culture & History (RICH). We currently have approximately 2,500 students, enrolled in three departments: the Department of History, Art History and Classics, the Department of Modern Languages and Cultures, and the Department of Language and Communication. We aim to contribute to a more sustainable and inclusive world, which is why we especially seek applications from candidates who bring diverse perspectives, backgrounds, and skills that will be assets to our study programmes and research profiles.


  • Employment for 1.0 FTE.
  • The gross starting salary amounts to €2,770 per month based on a 38-hour working week, and will increase to €3,539 in the fourth year (salary scale P).
  • You will receive 8% holiday allowance and 8.3% end-of-year bonus.
  • You will be employed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract).
  • You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment and help your family settle in Nijmegen.
  • Working for us means getting extra days off. In case of full-time employment, you can choose between 30 or 41 days of annual leave instead of the legally allotted 20.
Work and science require good employment practices. This is reflected in Radboud University's primary and secondary employment conditions. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself, for example, exchange income for extra leave days and receive a reimbursement for your sports subscription. And of course, we offer a good pension plan. You are given plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes.

Meer informatie

For questions about the position, please contact Dr Cristian Tejedor García, Assistant Professor at Alternatively, you can contact Prof. Martha Larson, Full Professor at