Research Fellow (Computer Science/Statistics/Electronics Engineering)
Core
Conduct research in probabilistic machine learning and GenAI, focusing on sequence model design, continual learning, recurrent memory, Bayesian modelling, and uncertainty quantification.
Role type
Research Fellow (Probabilistic ML & GenAI)
Builds
Novel architectures for adaptive memory updates, uncertainty quantification, and sequential decision making.
Domain
Artificial Intelligence / Machine Learning / Statistics
Deliverable
production ML models
Required skills
Probabilistic machine learning, sequence model design, continual learning, recurrent memory, Bayesian modelling, uncertainty quantification, experimental design, hypothesis formulation, machine learning systems
Preferred skills
GenAI industry engagement, open-source community collaboration, robotics integration
Technologies
Auto-regressive sequence models, Bayesian frameworks
Responsibilities
Conduct research in AI/ML domains, produce academic publications, collaborate on joint robotics and AI projects, supervise graduate students, engage with industry or academic partners
Seniority
Mid-level Research Fellow, hands-on IC