30 Science "Mathematics" uni jobs at Monash University

Ivy May Frary Mathematical Sciences Honours Scholarship

Ivy May Frary Mathematical Sciences Honours Scholarship Sir John Monash Scholarship for Achievement This scholarship has been generously gifted to Monash University by the Estate of Brian Frary in

Living AI

In collaboration with people from Monash materials engineering, neuroscience and biochemistry we are developing living AI networks where neurons in a dish are grown to form biological neural

Self-aware neural networks

learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and computational project. To do this project you would need to apply for a Monash

Conscious AI

into AI systems or mathematical and computational models of brain function. This project would be for someone who wishes to pursue a deeper understanding of humans and machines and the meaning this has

Group Manager, On-Premise Infrastructure and Private Cloud

technology roadmaps that align with the enterprise's strategic goals and operational metrics. Your leadership will be key in enabling ICT-enabled business process reforms through strategic stakeholder

Group Manager, Public Cloud

roadmaps that align with Monash’s strategic goals Leadership and Development: Lead, develop, and motivate a diverse team of IT professionals across all levels of technical, service, and managerial skills

Machine learning based kinetic modeling on the thermal decomposition of plastic waste via pyrolysis

Computer Science and/or Chemical Engineering or equivalent; 2) Excellent mathematical and programming skills, in particular in the areas of Artificial Intelligence (AI) and machine learning including deep learning

Motorola Solutions Scholarship

the following criteria: Current student enrolled in an undergraduate degree in the Faculty of Engineering or Faculty of Information Technology at a Monash campus in Australia. A continuing student

Safe Neuro-symbolic Automated Decision Making with Mathematical Optimisation

[1,2,3,4,5,6] have shown that the unknown transition models can be accurately approximated as neuro-symbolic (deep) neural networks which then can be compiled into mathematical optimisation models (e.g., MILP

Branch-and-Cut-and-Price Algorithms for Computing Cost-Effective and Time-Efficient Delivery Routes for Trucks and Drones

have a truck to land on. Required knowledge This project would suit a mathematics or computer science student with a background in combinatorial optimisation, operations research or mixed integer linear