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Statistics (cognitive neuroscience) PhD Projects, Programs & Scholarships

We have 5 Statistics (cognitive neuroscience) PhD Projects, Programs & Scholarships

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  Understanding Human Brain Dynamics: Statistical Machine Learning for EEG analysis (PennyWU20PSY2)
  Prof W Penny
Application Deadline: 28 November 2019

Funding Type

PhD Type

This is a part-time 0.5 FTE project linked with a 0.5 FTE employment contract as an EEG technician. This project will leverage recent advances in statistical machine learning to better understand the neuronal processes that support perception, cognition and action.
  Basic and Applied Motivation science - the function and nature of human motivation
  Dr K Murayama
Applications accepted all year round

Funding Type

PhD Type

Applications are invited for a PhD student position on motivation science, a new emerging interdisciplinary area that examines the nature of human motivation.
  Deciphering the neural code of the hippocampus
  Dr M Montemurro, Dr J Gigg
Applications accepted all year round

Funding Type

PhD Type

Background. The brain processes and stores information about our lives and the world that surrounds us. At the neural scale, information is encoded through the precise timing of activity across populations of neurons.
  A Neural Fuzzy Fusion Engine for Human-Machine Autonomous Systems
  Dr YK Wang
Applications accepted all year round

Funding Type

PhD Type

Abstract. This project aims to develop an intelligent engine to adaptively fuse multiple trust-based information from various agents in human machine autonomous systems (HMAS).
  Biologically-Plausible Continual Learning
  Dr K Chen
Applications accepted all year round

Funding Type

PhD Type

Continual learning (aka lifelong learning) refers to a problem on how a learning system learns multiple tasks in succession over the lifespan where later tasks do not degrade the performance of the system learned for the earlier tasks and, ideally, the system can leverage the knowledge learned in previous tasks to facilitate learning the new tasks better.
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