Automated Eating Activity Tracking System for Anorexia Nervosa Using AI and Wearable Sensors

19 Aug 2026
Job Information

Organisation/Company

KU LEUVEN

Research Field

Engineering » Biomedical engineering
Neurosciences » Neuropsychology
Computer science » Informatics
Engineering » Computer engineering

Researcher Profile

First Stage Researcher (R1)

Application Deadline

15 Oct 2026 - 23:59 (UTC)

Country

Belgium

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

2 Nov 2026

Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme

Reference Number

BAP-2026-436

Marie Curie Grant Agreement Number

0

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Anorexia nervosa (AN) is one of the most severe and persistent mental health disorders, characterized by profound disturbances in eating behavior and food-related cognition. Behavioral symptoms such as restrictive eating, meal avoidance, rigid eating patterns, prolonged meal duration, and excessive control over food intake are central features of the disorder and are closely linked to illness severity, treatment response, and relapse risk.
Despite the critical role of eating behavior in AN, current clinical assessment remains largely dependent on self-report measures, retrospective questionnaires, food diaries, and clinical interviews. Although these approaches provide valuable insights into patients’ experiences, they are limited by recall bias, social desirability effects, and the considerable cognitive and emotional burden they place on individuals with eating disorders. Importantly, they provide only intermittent snapshots of eating behavior and do not capture how patients actually eat in their daily lives.
Recent advances in artificial intelligence (AI) and wearable sensing technologies create new opportunities for objective, continuous, and ecologically valid assessment of eating behavior. Wrist-worn inertial measurement unit (IMU) sensors offer a promising approach to unobtrusively monitor hand-to-mouth movements associated with eating without the privacy concerns, stigma, or practical limitations associated with camera- or audio-based monitoring systems. However, existing wearable-based eating detection approaches have primarily been developed in healthy populations and controlled environments, with a strong focus on classification accuracy rather than clinical applicability, uncertainty estimation, and behavioral interpretability.
This PhD project aims to address this critical gap by developing an AI-enhanced wearable system for automated tracking and characterization of eating behavior in individuals with AN. The project will combine wearable sensing, advanced signal processing, machine learning, and longitudinal behavioral analysis to establish clinically meaningful digital biomarkers of eating behavior. These biomarkers will quantify fine-grained characteristics of eating patterns, including eating rate, temporal organization, behavioral rigidity, variability, and changes over time.
By integrating technology development with clinical expertise, this project seeks to enable objective monitoring of eating behavior in real-world settings and provide new tools for early identification of behavioral deterioration, treatment response, and recovery trajectories in AN.
As a PhD researcher, you will:•Design, optimize, and validate data acquisition protocols for wearable-based eating behavior monitoring.•Develop robust signal processing and machine learning pipelines for extracting eating-related behavioral patterns from IMU sensor data.•Develop interpretable digital biomarkers that capture key micro-structural properties of eating behavior in AN, including eating speed, temporal regularity, rigidity, and behavioral variability.•Investigate relationships between wearable-derived biomarkers and clinical outcomes, including illness severity, treatment progress, and recovery, with particular attention to within-person changes over time.•Collaborate closely with clinicians, psychologists, and researchers in a multidisciplinary environment.•Disseminate research findings through international peer-reviewed publications, scientific conferences, and outreach activities.
We are seeking a highly motivated PhD candidate with a strong interest in AI4Healthcare and a passion for applying AI and sensor technologies to improve dietary monitoring and health outcomes for patients with eating disorders. The ideal candidate is a team player, motivated to collaborate with the e-Media research lab, Mind-Body research group, clinical partners, and interdisciplinary stakeholders, and possesses:• A Master’s degree in Engineering (Computer Science, Artificial Intelligence, Electrical Engineering, Mechanical Engineering, Biomedical Engineering, or related fields) with excellent academic results.• Genuine interest in psychiatry, neuroscience, and clinical research, with motivation to engage with patients and real-world healthcare challenges.• Strong programming skills in Python; experience with deep learning frameworks such as PyTorch or TensorFlow is highly desirable.• Proven research ability, demonstrated through excellent academic records and a high-quality MSc thesis.• Excellent command of spoken and written English. Proficiency in Dutch is highly desirable, as the project involves interaction with clinical partners and participant-based data collection.• Willingness to participate in data collection and real-world experiments
We offer a fully funded, full-time PhD position within an innovative interdisciplinary research project focused on developing AI-driven digital biomarkers for monitoring eating behavior in anorexia nervosa:• Full-time PhD position (initial 1 year, renewable up to 4 years)• Contract will start from November 2nd , 2026 or as soon as possible hereafter.• Salary according to KU Leuven standards• Access to state-of-the-art research infrastructure and cutting-edge facilities• Advanced academic and interpersonal skill training through the Doctoral School program• Interdisciplinary and collaborative research environment• Training and mastering of advanced methods and transferable skills• Opportunities for interdisciplinary and (inter)national collaborations
For more information please contact Dr. Chunzhuo Wang, mail: [email protected] or Prof. Bart Vanrumste, mail: [email protected] .

Where to apply

Website

https://www.kuleuven.be/personeel/jobsite/jobs/60706508?hl=en

Requirements

Research Field

Computer science

Education Level

Master Degree or equivalent

Research Field

Engineering

Education Level

Master Degree or equivalent

Research Field

Information science

Education Level

Master Degree or equivalent

Research Field

Technology

Education Level

Master Degree or equivalent

Languages

ENGLISH

Level

Excellent

Languages

DUTCH

Level

Basic

Research Field

Engineering » Biomedical engineering

Years of Research Experience

None

Additional Information
Benefits

We offer a fully funded, full-time PhD position within an innovative interdisciplinary research project focused on developing AI-driven digital biomarkers for monitoring eating behavior in anorexia nervosa:• Full-time PhD position (initial 1 year, renewable up to 4 years)• Contract will start from November 2nd , 2026 or as soon as possible hereafter.• Salary according to KU Leuven standards• Access to state-of-the-art research infrastructure and cutting-edge facilities• Advanced academic and interpersonal skill training through the Doctoral School program• Interdisciplinary and collaborative research environment• Training and mastering of advanced methods and transferable skills• Opportunities for interdisciplinary and (inter)national collaborations

Eligibility criteria

We are seeking a highly motivated PhD candidate with a strong interest in AI4Healthcare and a passion for applying AI and sensor technologies to improve dietary monitoring and health outcomes for patients with eating disorders. The ideal candidate is a team player, motivated to collaborate with the e-Media research lab, Mind-Body research group, clinical partners, and interdisciplinary stakeholders, and possesses:• A Master’s degree in Engineering (Computer Science, Artificial Intelligence, Electrical Engineering, Mechanical Engineering, Biomedical Engineering, or related fields) with excellent academic results.• Genuine interest in psychiatry, neuroscience, and clinical research, with motivation to engage with patients and real-world healthcare challenges.• Strong programming skills in Python; experience with deep learning frameworks such as PyTorch or TensorFlow is highly desirable.• Proven research ability, demonstrated through excellent academic records and a high-quality MSc thesis.• Excellent command of spoken and written English. Proficiency in Dutch is highly desirable, as the project involves interaction with clinical partners and participant-based data collection.• Willingness to participate in data collection and real-world experiments

Selection process

For more information please contact Dr. Chunzhuo Wang, mail: [email protected] or Prof. Bart Vanrumste, mail: [email protected] .

Website for additional job details

https://www.kuleuven.be/personeel/jobsite/jobs/60706508?hl=en

Work Location(s)

Number of offers available

1

Company/Institute

KU LEUVEN

Country

Belgium

City

Leuven

Contact

City

Leuven

STATUS: EXPIRED

• X (formerly Twitter)
• Facebook
• LinkedIn
• Whatsapp
• 
  More share options
  • E-mail
  • Pocket
  • Viadeo
  • Gmail
  • Weibo
  • Blogger
  • Qzone
  • YahooMail