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.
areasWe are pushing the frontiers of AI by:
÷ Unlocking intelligence,
÷ Designing competent, beneficial intelligent machines, and
÷ Transforming physical and digital fields through AI.
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
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
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.
want to use AI to make the world better. Here are a few things we are
÷ Accelerating scientific
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
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.
in what we want to read the code, fix the bugs, synthesize programs,
the programming process, understand developer and support team
management. See this
page for the latest update on our research.
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:
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.
experience (e.g. has published papers).
recognition: university prizes and awards. High quality honours at high
school (such as members of national or international
teams) may also count.
in CS/Maths/Physics with excellent
academic performance from a well-recognised university. For those with
Australian qualifications, the standard requirement is an Honours
math background (we will use a lot of statistics, optimization and
programming skills. An ability to scale up to large datasets is a bonus.
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
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
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:
comprehensive resume (or CV);
papers or technical reports (if written in languages other than
English, titles and abstracts should be translated to English);
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.
can send your documents directly to Professor Truyen Tran.
updated: Jan 8th 2024