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PhD  on Small Multimodal Models for  Low-Resource Edge Devices
Eindhoven University of Technology

PhD on Small Multimodal Models for Low-Resource Edge Devices

2025-06-29 (Europe/Amsterdam)
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Sobre el empleador

We are an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude.

Visita la página del empleador

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.

The Department of Industrial Design conducts research on and education in the design of systems with emerging technologies in a social context. We excel at integrating various academic disciplines, including engineering, design, business, and social sciences.

Job Description

The Decentralized Artificial Intelligence Research Lab (DARL) at the Eindhoven University of Technology is seeking a talented and passionate Ph.D. candidate to join our team. Our mission is to revolutionize the field of Artificial Intelligence (AI) by developing cutting-edge collaborative learning techniques that enable AI models to learn from large-scale decentralized data. Our ultimate goal is to instill self-learning capabilities in globally distributed computational devices for everyday use.

The field of AI has seen unprecedented advancements in recent years, driven by the development of foundation models that have expanded the boundaries of machine capabilities. However, learning

these models requires direct access to vast data repositories, which poses significant privacy and logistical challenges, especially in the health sensing domain that involves personal data. To address this, the DARL is at the forefront of research exploring decentralized and collaborative approaches to developing unified AI systems. Our research entails the development of novel methodologies at the intersection of audio language models, self-supervised learning, data-centric machine learning, and human-machine collaboration for healthcare and high-tech industries.

We are currently seeking a candidate for a PhD position focusing on a small multimodal model for audio (and speech) understanding.

Successful candidate will have the opportunity to work with a dynamic and interdisciplinary team of researchers, collaborating with experts in AI and healthcare.

Job Requirements

  • A master's degree (or an equivalent university degree) in Computer Science, Mathematics, Machine Learning or a related technical field.
  • Strong background in deep learning with a motivation to advance fundamental techniques.
  • Ability to work independently and persistently tackle difficult research problems.
  • Has a solid interest in pushing the frontier of one or more of the following: a) audio and speech understanding with small multimodal models, b) on-device learning, c) pre-training strategies, and d) deployment and evaluation on devices like smartphones.
  • Excellent analytical, problem-solving, and software engineering skills with prior experience implementing machine learning algorithms using well-known frameworks (e.g., PyTorch).
  • Collaborative spirit and ability to work productively as part of a multidisciplinary team.
  • Strong communication skills, including proficiency in written and spoken English (C1).

Conditions of Employment

  • A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you: 
  • Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
  • Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 2,901 max. € 3,707).  
  • A year-end bonus of 8.3% and annual vacation pay of 8%. 
  • High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process
  • An excellent technical infrastructure, on-campus children's day care and sports facilities.  
  • An allowance for commuting, working from home and internet costs. 
  • A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager, dr. Aaqib Saeed, Assistant Professor, [email protected].

Visit our website for more information about the application process or the conditions of employment. You can also contact [email protected].

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

  • Cover letter in which you describe your motivation and qualifications for the position.
  • Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Type of employment: Temporary position
Contract type: Full time
Salary: Scale P
Number of positions: 1
Full-time equivalent: 1.0 FTE
City: Eindhoven
County: Noord-Brabant
Country: Netherlands
Reference number: 2025/193
Published: 2025-05-12
Last application date: 2025-06-29

DESCRIPCIÓN DEL PUESTO

Título
PhD on Small Multimodal Models for Low-Resource Edge Devices
Ubicación
De Zaale Eindhoven, Holanda
Publicado
2025-05-12
Fecha límite de aplicación
2025-06-29 23:59 (Europe/Amsterdam)
2025-06-29 23:59 (CET)
Tipo de trabajo
Guardar trabajo

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