PhD Position in Data-Efficient Machine Learning: Join us in exploring the context-sensitivity of data-driven approaches for affect prediction from human behavior. This fully-funded PhD position at the TU Delft offers a dynamic, stimulating, and diverse research environment, focusing on data-efficient algorithms to address the context-dependence of emotional processes in real-world settings such as healthcare.
Fully Funded PhD Position in Data-Efficient Machine Learning for Context-Sensitive Affective Computing
Designation: PhD Candidate
Research Area: Data-Efficient Machine Learning for Context-Sensitive Affective Computing
Location: Delft University of Technology, Delft, Netherlands
Eligibility/Qualification:
- A Master’s degree or equivalent (or about to graduate with one) in a relevant field (Artificial Intelligence, Computer Science, Data Science, Cognitive Science, etc.)
- Experience with machine learning/deep learning and quantitative research methods through coursework or projects.
- Strong analytical and conceptual modeling competencies.
- Good programming skills (preferably Python), including ML methods and libraries.
- Excellent (written and verbal) proficiency in English.
Preferred optional qualifications:
- Experience with multimodal affective computing models or user-modeling techniques
- Experience in collecting multimodal datasets or running experiments with participants
- Experience with interdisciplinary research projects
- Familiarity with different Theories of Emotion (e.g., Cognitive Appraisal Theories, Constructivist Theories, etc.)
Job Description:
- Explore how different context characteristics captured in training datasets influence the generalizability and data efficiency of multimodal machine learning approaches for automatic affect prediction.
- Develop a suitable methodology for such an investigation, potentially involving designing and collecting new datasets involving human participants.
- Focus on advancing data-efficient or robust machine-learning techniques (e.g., learning with privileged information or meta-learning) to better address relevant aspects of context sensitivity.
How to Apply:
- Interested candidates can apply before 30th October 2024 via the application button on the webpage, and upload the following documents:
- Motivation letter (max 2 pages)
- CV (max 2 pages)
- Academic transcripts (both MSc and BSc degrees)
Last Date for Apply: 30th October 2024
