Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
-
that unveil the unseen. analyse complex data to uncover insights and pioneer new strategies for molecular sensing and imaging. We’re looking for someone who brings: A PhD in chemical biology, organic chemistry
-
, conferences, or equivalent textbooks/teaching resources Ability to lead outstanding research and manage a research team and projects Demonstration of effective PhD or Honours student supervision Proven success
-
through supervision of PhD students, and contribute to the educational and administrative duties of the Department and Faculty such as by taking on course direction roles. As the successful candidate, you
-
is highly complex. For the proposed PhD project, experimental data are already available that bring together maps of orientations of such crystals together with the deformation pattern generated during
-
ecosystem interactions. If used wisely for decision-support, these technologies can help select and implement effective policies. This PhD project, jointly offered by Monash University (Australia) and
-
closely with academic staff and provide guidance and support to junior researchers and students involved in the project. Ideally you will have a PhD in Economics of Education or expect to receive it by
-
Cybersecurity is an interdisciplinary field. There is an urgent need to build up talent in human factors in cybersecurity. This PhD will provide the candidate with a unique pathway into industry
-
The proposed PhD project aims to build a machine learning/deep learning-based decision support system that provides recommendations on precision medicine for paediatric brain cancer patients based
-
This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain