Sort by
Refine Your Search
-
Employer
-
Field
-
, in the Faculty of Business & Law at Curtin University, is seeking one (1) doctoral applicant (PhD) to join an Australian Research Council [ARC] Discovery Project, namely: Optimizing Benefits
-
of geometric flows to resolve important geometric and topological questions, and produce new theoretical tools applicable to similar systems arising in areas such as image processing, finance and material
-
Development of auxetic structures for optimal orthopaedic implant / bone integration 3 Minute read This PhD project will be based at the University of Melbourne with a 12-month stay at Shanghai Jiao
-
I supervise a wide range of PhD projects on experimental research into the electronic properties of novel quantum materials including topological insulators, graphene, and other atomically thin two
-
@rmit.edu.au Please send your CV to akram.hourani@rmit.edu.au Required Skills: Programming and simulation: strong experience in Python or MATLAB. Mathematical modelling: probability, optimization, or multi-agent
-
Optimizing a 3D microfluidic IVD model to study cell responses to wear particles, refining culture conditions, and analysing cytotoxic and inflammatory mechanisms. Optimizing a 3D microfluidic IVD
-
to simulate sewer networks as dynamic systems, targeting ≥90% modelling accuracy. Train an explainable decision-making agent to optimize interventions (e.g., pipe upgrades), balancing cost, equity, and
-
the optimal habitat reconstruction for declining bird species, how to best engage community and landholders in the recovery effort, and priority species-specific objectives for guiding restoration actions and
-
, and friendliness. The ARC Industrial Transformation Training Centre for Optimal Ageing and Monash University The successful candidate will be a member of the Industrial Transformation Training Centre
-
into neural networks. PINNs can model real-world signals with sparse, non-uniform, and noisy data. A key question is determining the optimal method for integrating physical priors into neural networks