Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
-
2025. Encouraged by the continuing success of modern machine learning (ML) techniques, researchers have become ambitious to develop ML solutions for challenging science and engineering problems with
-
which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
-
of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
-
effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
-
datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
-
(Wissenschaftszeitvertragsgesetz - WissZeitVG). A shorter contract term is possible by arrangement. The position aims at obtaining further academic qualification. Professional assignment: Chair of Machine Learning for Spatial
-
PhD Scholarship Opportunities in Optoelectronic Semiconductors at the Faculty of Engineering and Faculty of Science (Multiple Positions) Job No.: 682543 Location: Clayton campus Employment Type
-
student, you will be supported by a multidisciplinary team with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected
-
approaches, including Low Impact Development (LID) practices (e.g., green roofs, rain gardens), with a specific focus on urban catchments. The research will place a strong emphasis on machine learning