324 computer-engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Monash University
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discover them The Opportunity The Department of Electrical and Computer Systems Engineering at Monash University is seeking an outstanding and highly motivated Research Fellow to join its internationally
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We are excited to offer a fully funded PhD position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with
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In many branches of science (e.g., Artificial Intelligence, Engineering etc.), the modelling of the problem is done through the use of functions (e.g., f(x) = y). On a very high-level, we can think
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possess translational symmetry, the role of structure and symmetry in glasses is not established. This research programme involves the development of new x-ray and electron diffraction-based methods
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
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at Monash. The program aims to support Pakistan’s need for highly qualified engineers, technologists, and scientists in high-tech fields, as well as build up capacity of their universities, research and
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capability. Details of the relevant requirements are available at www.monash.edu/graduate-research/study/apply . A degree in Chemistry, Chemical Engineering, Materials Science Electrochemistry, Physics, or a
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computation is necessary before taking an action. This PhD project will address these challenges by developing trustworthy and resource-adaptive VLA models. A central question is whether an embodied agent can
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explore current techniques such as fine-tuning, model alignment, prompt engineering and Retrieval Augmented Generation (RAG) to improve reliability of generated recommendations for two cases of chronic