Publisher
source

Alexander Mielke

11 months ago

Syntax of Facial Communication: Statistical and Machine Vision Approaches to Segmenting and Understanding Facial Signals Queen Mary University of London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD Project (Students Worldwide)

Deadline

Expired

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Country

United Kingdom

University

Queen Mary University of London

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Keywords

Computer Science
Data Science
Psychology
Artificial Intelligence
Open Science
Computer Vision
Python Programming
Psycholinguistics
R Programming
Applied Behavior Analysis
Syntax
Nonverbal Communication
Social Communication
Computational Modelling
Video Compression
Statistical Modelling
Statistic
Linguistics
Facial Expression

About this position

This PhD project at Queen Mary University of London, supervised by Dr Alexander Mielke and Professor Isabelle Mareschal, investigates the syntax of facial communication using a combination of psychological, linguistic, machine vision, and statistical approaches. Unlike speech and gestures, which can be segmented into clear units and studied for syntactical rules, facial communication is complex due to the rapid and independent movement of different facial parts. The project aims to develop statistical methods to segment complex facial muscle movement patterns into discrete units, analyze sequences of naturally occurring facial signals, and explore how the combination of facial signals in time influences social interactions.

The research is highly relevant for advancing human-machine communication and the automatic categorization of facial signals. The successful candidate will join the Mielke Lab, which focuses on understanding communication and cooperation in humans and non-human primates through observational studies and innovative data analysis methods.

Training will include observational data collection, video coding, computational and statistical modelling, programming in R and Python, and academic writing, with a strong emphasis on open science practices. The position is part of a joint scholarship programme between Queen Mary and the China Scholarship Council (CSC), offering full tuition coverage and living expenses for eligible Chinese applicants. Applicants must hold or expect to receive a first or upper-second class honours degree and a Master’s degree in a relevant field, meet English language requirements, and submit all required documentation by the deadline. The application process includes a formal online submission, identification as a CSC Scholar, and an interview for shortlisted candidates.

The project provides an excellent opportunity to develop interdisciplinary skills in psychology, linguistics, data science, and machine vision within a supportive research environment.

Funding details

Funded PhD Project (Students Worldwide)

What's required

Applicants must be applying for CSC funding, be a citizen and permanent resident of the People’s Republic of China, and hold a Chinese passport. Candidates should have or expect to receive a first or upper-second class honours degree and a Master’s degree in a relevant area such as Psychology, Linguistics, Data Science, Statistics, or Computer Science. Knowledge of psycholinguistics, coding in R and/or Python, machine vision models, or behavioural coding is advantageous but not required. Applicants must meet the IELTS requirements for the course and upload evidence before the CSC application deadline, ideally by 1st March 2026. All required documentation, including CV, personal statement, English language evidence, academic transcripts, degree certificates, and references, must be submitted by the application deadline.

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Professors

Dr. at School of Biological and Behavioural Sciences

University Name
Country flag

United Kingdom

Research Interests

Behavioral Neuroscience

Biological Anthropology

Primatology

Animal Behavior

Social Behavior

Professor at School of Biological and Behavioural Sciences

University Name
Country flag

United Kingdom

Research Interests

Psycholinguistics

Artificial Intelligence

Psychology

Child Psychology

Video Compression