One master internship in measuring human eating behavior

[Saive, Anne-Lise]

💥 Master internship in Cognitive and Computational Neuroscience 💥

               Measuring human eating behavior in naturalistic settings

📍Between EPFL (Geneva, Switzerland) & Institut Paul Bocuse Research Center you'll have the chance to

work with a multidisciplinary team including Pr Alexander Mathis and myself but also many chefs and cooks !

DEADLINE: Dec 1st, 2022 (internship starting January-February 2023)

 

DESCRIPTION - One master internship in measuring human eating behavior

 

A joint master internship is available in the Mathis Group at EPFL and the Food Cognition Group at IPBR

to measure human eating behavior from video-recordings using machine-learning and computer vision

approaches. This position is not funded but travels to visit IPBR in France will be covered.

 

Overview: Eating is a daily and essential activity that implicitly encompasses a lot information. Our

eating behavior is a result of our physiological needs, past experiences, as well as social and cultural

environment, but it can also reflect our health status. Despite evidence linking changes in eating habits

and the development of psychiatric disorders, we are still lacking a robust and fine-grained measures that

can reliably detect the early signs of these diseases. The aim of this project is to advance this search using

naturalistic video-recordings of healthy participants eating at IPBR restaurants. This research will leverage

advanced machine-learning analyses and computer vision techniques to identify actions, as well as implicit

information during eating.

 

Ideal candidate

• Holds a bachelor’s in computer science, neuroscience, biomedical sciences or related fields

• Strong programming skills in Python

• Experience with machine learning libraries would be an important asset

• Strong interest in neuroscience and cognitive science

• Team spirit & excellent communication and organizational skills

• Good writing skills in English

 

How to apply?

Motivated individuals interested in joining us should upload a single pdf document with a cover letter

stating the past and future research interests, CV and publication list, and a list of up to 2 references at

alexande...@epfl.ch and anne-li...@institutpaulbocuse.com.

We are strongly committed to equity and diversity. We welcome applications from racialized

persons/visible minorities, women, Indigenous persons, persons with disabilities, ethnic minorities, and

persons of minority sexual orientations and gender identities.

A selection will be made among eligible applicants on an overall basis, and candidates selected for an

interview will be contacted.

If you have any questions about this opportunity, please send an email to any of the two supervisors

listed above.

 

MORE INFORMATION

Research Center - Institut Paul Bocuse in Lyon, France

The IPB research center conducts cutting-edge research on food practices and gastronomy thanks to its

teams of researchers, innovation engineers and gastronomy professionals (chefs, pastry chefs, bakers,

sommeliers etc.). From basic research to applied projects, we develop unique interdisciplinary

approaches to questions related to food and culinary arts. In the Cognition & AI team, we are

particularly interested in the early detection of eating disorders and in the development of innovative

ways to promote culinary and olfactory expertise in both naive and expert populations using advanced

ML tools and VR/AR setups.

Mathis Lab in Geneva, Switzerland (EPFL)

The lab works at the intersection of computational neuroscience and machine learning. Ultimately, we

are interested in reverse engineering the algorithms of the brain, in order to figure out how the brain

works and to build better artificial intelligence systems. We strive to develop tools for the analysis of

animal behavior. Behavior is a complex reflection of an animal's goals, state and character. Thus,

accurately measuring behavior is crucial for advancing basic neuroscience, as well as the study of various

neural and psychiatric disorders. However, measuring behavior (from video) is also a challenging

computer vision and machine learning problem. Thus, our work will build on advances in machine

learning and computer vision to push the envelope for the analysis of behavior