Embark on a cutting-edge Ph.D. research project at the University of Adelaide, specifically focusing on maximizing mill throughput through the application of machine learning techniques and evolutionary algorithms. Led by Dr. Lei Chen, join a dynamic team of esteemed co-supervisors to presently contribute to advancing predictive grinding circuit models for enhanced efficiency in mining operations. The project involves real-time analytics of particle size distributions and grinding circuit sensors to develop an online model for optimization.
Tasks:
- Analyze real-time data collected by circuit sensors to develop predictive grinding circuit models.
- Implement and refine optimization methods based on sensor inputs as well as including evolutionary algorithms and bio-inspired optimization approaches.
- Collaborate with the research team to integrate findings into operational strategies for maximizing mill throughput.
- Engage in regular discussions and progress updates with the principal supervisor and co-supervisors.
Qualifications
Prospective candidates should generally possess a strong background in mining, engineering, or related disciplines. A Master’s degree or equivalent in a relevant field is required. Prior experience in machine learning, evolutionary algorithms, or process optimization will be advantageous.
Application Process: Interested candidates are invited to send their applications, including a detailed CV and a cover letter expressing their motivation and relevant experience, to the Principal Supervisor, Dr. Lei Chen, at lei.chen@adelaide.edu.au. Please include “Ph.D. Application – HDR11 Project” in the email subject.
Application Deadline: March 15, 2024
Seize this opportunity to contribute to cutting-edge research in mining engineering and optimize efficiency in mining operations. Apply now for the Doctoral Degree Scholarships at the University of Adelaide.
For more detailed information about the application process, click here
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