31 machine-learning positions at Ulster University

Transformative Applications of Quantum Machine Learning in Digital Healthcare

for variational quantum circuit based functional regression. [5] M. Schuld et al., The effect of data encoding on the expressive power of variational quantum machine learning models. [6] A. Zeguendry et al

Machine Learning inspired Channel Estimation in Future Wireless networks

years, machine learning (ML) methods are found to be effective in wireless communication, particularly for channel estimation in RIS integrated NOMA communication system. However, still required further

Machine Learning and Statistical Approaches for Understanding Vulture Behavioural Ecology

of the global population in some species (e.g., Griffon vultures or Bearded vultures). The proposed research aims to utilize advanced machine learning techniques to unravel the intricate behaviours and

AI Technology for Smart Labelling and Root Cause Analysis in a Semiconductor Wafer Manufacturing Process

our mission we use a variety of research strategies that include big data and machine learning, brain imaging and neural interfacing, human-computer interaction and robotics. Bioinspired artificial

Causality and Counterfactuals in Explainable AI

, The seven tools of causal inference, with reflections on machine learning, Commun. ACM 62 (2019), 54-60. [3] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, Causability and explainability

Towards Trusted Cognitive Intelligence for User-centric Smart Systems

Meet Artificial Social Intelligence, IEEE Internet of Things, DOI: 10.1109/JIOT.2021.3081556, 2021. F. Yang, Z. Yu, L. Chen, J. Gu, Q. Li, B. Guo, Human-Machine Cooperative Video Anomaly Detection

Continual learning for modelling non-stationary systems

system in a magnetic shielded room (MSR)). The candidate will benefit from Ulster’s wide-ranging expertise in Computational Neuroscience, Cognitive Neuroscience, Machine Learning and Computational Biology

Deep Learning Multimodal Emotion Recognition

-level fusion in combination with machine learning algorithms to achieve emotion recognition with some success. However, more recently deep learning has shown remarkable capabilities in learning detailed

Development of Change Transformer—Attentive Deep-Learning Approaches for Abnormal Change Detection

Summary Deep learning is a hot topic in machine learning and it is playing increasingly important roles in intelligence systems for our daily lives, such as computer vision, autonomous car driving

Cognitive Computational Neuroscience: Emergent decision and related computation with biological recurrent neural networks

learning algorithms inherently perform decision-making, the developed neural model(s) will in turn inspire the development of advanced machine learning algorithms. This timely and exciting project is