Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
-
the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
-
). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
-
A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty
-
filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
-
. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
-
AI hardware beyond traditional computing architectures. Gain a unique combination of skills in mathematics, machine learning, and photonics. Be part of a multidisciplinary research team spanning
-
(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
-
. Supported by major research grants, the School of Computer Science at UNNC is developing research excellence in areas including Machine Learning, Big Data, Visual Analytics, Computational Intelligence
-
, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning