Sort by
Refine Your Search
-
Employer
-
Field
-
analysis and data processing. Strong programming skills in R (preferable) and/or Python, and experience or interest in weather prediction or climate models. Knowledge of machine learning, AI techniques, 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
-
analysing large-scale datasets such as StatsBomb, which provide detailed technical and tactical data across multiple leagues and seasons. By applying advanced analytical and machine learning techniques
-
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
-
Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data
-
@rmit.edu.au Dr. Shao, Wei (Data61, Marsfield) - wei.shao@data61.csiro.au The successful candidate is expected to have strong motivation and evidenced skills in machine learning and computer vision
-
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
-
(www.ledbyexperience.org) and network of collaborators in a recent review stated that societal issues of climate change, military conflict, and criminality, are inevitably connected with those of mental health and well
-
project management skills. Candidates with strong skillset, including familiarity with structural health monitoring, computer vision and machine learning are desired for this project. Must be eligible
-
reliable. This project will be supported by a robust infrastructure and an intellectually stimulating environment within our machine learning group. The PhD student will be supervised by two highly