PhD in Multimodal Sensor Systems and AI-Based Analysis for Objective Olfactory Assessment

Introduction

This PhD position is intended for candidates with a strong interest in sensor development and multimodal signal processing who are motivated to work in a multidisciplinary environment at the interface of engineering and clinical research.

Job Description

Human olfaction is critical for functions such as hazard detection food perception and personal hygiene. However olfactory disorders affect nearly 20% of the population and can substantially diminish quality of life. A key limitation of current clinical assessment methods is their strong reliance on subjective patient reporting leading to limited reliability poor reproducibility and restricted insight into the underlying physiological mechanisms.

In this project you will tackle this problem by designing and developing a novel sensing and data-driven evaluation system for olfactory function. You will engineer a sensor setup capable of topographically mapping nasal sensory responses and integrate it with controlled stimuli generated by an olfactometer. Multiple biosignals (including EEG negative mucosal potential and peak nasal inspiratory flow) will be synchronously acquired and fused using an AI-based signal processing and modeling framework. Patient-specific parameters (e.g. age sex comorbidities allergies smoking history) will be incorporated to enable personalized and objective assessment.

The developed system will be validated in a clinical study investigating the effects of endoscopic endonasal skull base surgery on olfactory function. Postoperative smell loss exhibits high inter-patient variability making it an ideal use case for advanced sensing multimodal data fusion and intelligent analysis.

Job Requirements

• A masters degree (or equivalent university degree) in Electrical Engineering Mechanical Engineering Robotics Biomedical Engineering or related field.
• Strong background in sensor systems signal processing and/or measurement techniques preferably in physiological or experimental settings
• Familiarity with machine learning or AI-based methods for multimodal data analysis and pattern recognition
• A strong research-oriented mindset with motivation to develop novel methods and validate them experimentally
• Ability and willingness to work in a multidisciplinary team collaborating with clinicians neuroscientists and engineers.
• Motivation to contribute to teaching activities and to supervise students
• Fluency in spoken and written English (C1 level)
  

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 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. 3059 - max. 3881).
• 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 childrens 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.

About us

Eindhoven University of Technology is a leading international university within the Brainport region where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact today and in the future. TU/e is home to over 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

Information

Do you recognize yourself in this profile and would you like to know more Please contact the hiring managerJulie Legrand Assistant Professor .

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

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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. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. We give priority to complete applications.

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.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.