The ability to capture carbon is expected to play a key role in the fight against climate change. A new apparatus is being developed and built at Monash University to capture carbon dioxide from the air – enriching it from 410ppm to > 95% purity. This important technology, when scaled up, provides a pathway to decrease the CO2 concentration in the atmosphere. To improve our design and operation of this technology, a numerical model needs to be developed and an optimisation and control framework developed to validate and understand the operation of the experimental unit.
Honours and Minor Thesis projects
Machine Learning (ML) models are deployed in many safety-critical systems (such as self-driving cars, cancer detection software, etc.) to improve human decision-making. Therefore, safety is central to the success of many human-in-the-loop systems that deploy such ML models.
Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using efficient algorithms running on a special type of computer based on the principles of quantum mechanics, known as a quantum computer. Due to significant recent advances in quantum computing technology, this threat may become a practical reality in the coming years. To mitigate against this threat, new `quantum-safe’ (a.k.a.
Propositional satisfiability (SAT) is a well-known example of NP-complete problems. Although NP-completeness may be perceived as a drawback, it allows one to solve all the other problems in NP by reducing them to SAT and relying on the power of modern SAT solvers. This is confirmed by a wealth of successful examples of use cases for modern SAT solving, including generalisations and extensions of SAT as well as a wide variety of practical applications in artificial intelligence (AI).
Mini-CP https://www.info.ucl.ac.be/~pschaus/minicp.html is a minimal form of constraint programming solver, designed to allow for easy experimentation and learning.
One of the most efficient approaches to discrete optimisation solving is using lazy clause generation, which is a hybrid SAT/CP approach to solving problems. But MiniCP does not currently support this.
Given a knowledge base describing the existing background constraints and assumptions about what is possible in the world as well as the prior experience of an autonomous agent on the one hand and probabilistic perception of the current state of the world of the autonomous agent, on the other hand, it is essential to devise and efficiently enumerate the most consistent world models that are likely to be valid under the prior knowledge in order to refine the agent’s up-to-date perception and take the most suitable actions.
Building a robust and trustworthy (semi-)autonomous agent requires us to build a consistent picture of the state of the world based on the data received from some perception module.
Please note this advert is for a Internship. It is not currently an advertisement for an honours or masters thesis project.
Please note you can ONLY apply for this internship via the internship application form.
In this project, you will build an autonomous agent in the MineRL environment for playing Minecraft or an agent for Animal-AI. Herein, you will learn how to incorporate symbolic prior knowledge for improving the performance of an agent trained by using deep reinforcement learning (RL) technique, which is the core technique to build AlphaGo.
Note: this is advertising for summer 2022 internship project (not an Honours Project)
The energy industry is evolving, and transiting to a new era with renewable energy being at the forefront. Making Australia aware of the lessons from the past and the predictions for the future is essential for us to start to understand how the country is changing for the better and what still needs to be done to ensure a more sustainable energy future for the population.