Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
, Engineering or others related to the PhD topic) Excellent programming and/or robotics background, with a keen interest in human-robot interaction Prior knowledge of robotics and machine learning (e.g., relevant
-
Building tools to detect or prevent unsafe AI outputs Exploring regulatory gaps and proposing solutions This is an ideal opportunity for candidates with interests in machine learning, public health, ethics
-
computer vision and machine learning methods to interpret the photovoltaic (PV) solar farm's condition and perform various inspections and anomaly detection. The research will draw from state-of-art
-
or climate models. Knowledge of machine learning, AI techniques, and cloud computing for data processing and model deployment. Familiarity with crop models (APSIM, DSSAT, AquaCrop), irrigation systems, and
-
microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
-
to peer-reviewed academic publications Qualifications Completed undergraduate degree in physics, computer science, machine learning, computational modelling, or similar. About Swinburne University
-
that combine fairness, privacy and legal guarantees for ADM systems, such as recommender and machine learning based systems. It takes a multi-disciplinary approach and although focused on the mobilities and
-
Vision and Edge Computing'. PhD candidates involved in this project will be trained in the emerging field of smart infrastructure, which is critical for Australian society in the coming decade
-
government background checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements. Selection criteria To be eligible applicants must: Have a basic understanding of machine learning
-
publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data