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PhD Scholarships in AI/ML

The Applied Artificial Intelligence Institute (A2I2) at Deakin University, Australia currently offers multiple full 3-year PhD scholarships to study AI/ML.

Research areas
Selection criteria
What to send

Research areas

We are pushing the frontiers of AI by:
÷ Unlocking intelligence,
÷ Designing competent, beneficial intelligent machines, and
÷ Transforming physical and digital fields through AI.

AI Fundamentals

We build systems that are smart, competent, energy-efficient and human-compatible. Major areas:

÷ Machine reasoning in that we find efficient ways for learning to reason with minimal human supervision, towards System 2 capability of high-order reasoning. Our sub-areas include, but not limited to: causal inference, systematic generalization, graph neural networks, relational models, memory and attention, and reasoning in Generative AI. See our IJCAI-2021 tutorial for a broad view of neural machine reasoning.

÷ New kinds of machine learning in that we explore novel forms of computation and learning to improve the lifelong adaptation with small energy footprints. This includes, but not limited to, small-but-powerful language/foundation models, learning to explore, human-in-the-loop, self-supervised learning, knowledge-guided learning, prompt optimisation, distillation, continual learning, novelty seeking, intrinsic motivation, and physics-informed ML. See our presentation on assessment of Generative AI in 2023.

÷ Multi-agent reinforcement learning in that an agent interacts/teams with others and humans. Equipped with deep nets for perception, memory, statistical relational learning, theory of mind, common sense, knowledge and reasoning capabilities, we aim to bring RL to a new level.

÷ Unifying vision and language in which we want to model, understand and reason about images and videos, answer natural questions, chat about movies, monitor streets, and understand human behaviors in video. In the long run, we aim to build a lifelong digital companion that can hold meaningful, long conversations with us.

÷ Human-compatible ML in which we want to develop novel machine learning frameworks to bring human perspectives into AI to mitigate negative impacts caused by unintended consequences if unchecked. The significance is in advancing our understanding of the emergent and vital field of AI alignment. Expected outcomes include new frameworks for machines to rapidly learn from, and reason about, human preferences and actively pursue benefits for humans as intended. Expected benefits are advanced capabilities for effective human-machine partnership, pushing the frontier of responsible AI.

AI Applications

We want to use AI to make the world better. Here are a few things we are passionate about:

÷ Accelerating scientific discovery in which we use AI to replace hard physical computation and experiments such as computing quantum chemical properties, predicting molecular properties & interactions, predicting chemical reactions, understanding the structure and characteristics of materials, searching for new alloys, and generating molecules & crystals. Part of these is accelerating new product development by finding best configurations for producing new products. See recent works here.

÷ Healthcare in which we aim to (a) understand about patient's health, need, preference and intention through patient's diverse sources of data (medical records, -omics, user-generated media, medical imaging and biosignals); (b) acquire and reason about established medical knowledge; (c) have a meaningful dialog with patients; (d) recommend the personalized course of medical actions; (e) support doctors and hospital managers to improve their precision and efficiency. See this blogpost for some futuristic ideas and this page for works done by us so far.

÷ Energy storage in which we search for new science and engineering of battery design, characterisation, diagnosis, prognosis, optimisation and manufacturing through the integration of model-driven physics/chemistry and data-driven AI. The broader picture includes green energy, climate change and sustainability.

÷ Drug discovery in which we aim to model the entire drug-like space, compute bioactivity, assess adverse effects, predict binding to targets, and best of all, generate new drugs with a given set of desirable properties. See for our tutorial at ECML 2021.

÷ Software engineering in what we want to read the code, fix the bugs, synthesize programs, translate between languages, automate the programming process, understand developer and support team management. See this page for the latest update on our research.

Selection criteria

There is no single selection criterion and we can only say that we look for candidates who have potential to have a successful research career, not just a PhD degree. If you do not know what it takes for a research career, I recommend that you should spend time to read about it. Sometimes it will help you decide whether you need a PhD or not. Here is the list of things we are looking for:

  • Thirst of conquering the unknowns and willing to pursue a research career. PhD is just a stepping stone, and a by-product of the learning process.
  • Research experience (e.g. has published papers).
  • Academic recognition: university prizes and awards. High quality honours at high school (such as members of national or international teams) may also count. 
  • Bachelor/Master in CS/Maths/Physics with excellent academic performance from a well-recognised university. For those with Australian qualifications, the standard requirement is an Honours degree. 
  • Good math background (we will use a lot of statistics, optimization and linear algebra).
  • Good programming skills. An ability to scale up to large datasets is a bonus.
  • Good machine learning & AI background. As a general rule of thumb, a good AI/ML PhD student will be likely to have covered statistical machine learning & probabilistic graphical models. A prior exposure to deep learning is a great bonus.
  • Good English (generally IELTS score 6.5, no bands below 6, is required for entry, but you should be aware that this score is insufficient for smooth communication). Good critical reading skills are vital because you will spend up to 50% of your time for reading. Technical writing is also important because it is often the end-product for assessment, and it accounts for 50% chance of acceptance.

What to send

The rule of thumb is that you should send whatever (electronic) documents you think that will support your future research career. Documents are written in English and may include some of the following:

  • A comprehensive resume (or CV);
  • Academic transcripts;
  • Theses;
  • Published papers or technical reports (if written in languages other than English, titles and abstracts should be translated to English);
  • A statement of purpose briefly describes your research intention and interests, general approach to reach your PhD/career goals;
  • Proposal for the intended thesis; and
  • Reference letters from your professors.

You can send your documents directly to Professor Truyen Tran.

Last updated: Jan 8th 2024