Design, train, and fine-tune machine learning models, specifically focusing on medical image segmentation tasks, utilizing state-of-the-art techniques in deep learning.Perform extensive data preprocessing, including the handling of large-scale medical imaging datasets, image augmentation, and annotation for model training and evaluation.Participate in the publication of research findings in peer-reviewed journals and conferences, contributing with the latest advancements in the field.

Conducted weekly tutorial and workshop sessions, reinforcing students' understanding of key concepts.Collaborated with the instructional team to review and update course content, ensuring materials were current, relevant, and engaging.Graded assignments, quizzes, and exams, providing timely and constructive feedback to support students' academic development.

Lead a critical project to develop a web application that uses machine learning to detect degradation in water pumps, enhancing maintenance efficiency across 200+ stations in South Australia. Designed and implemented an ETL pipeline to process complex datasets from the client's database, preparing them for machine learning analysis with Microsoft SQL server.Developed an LSTM model using Pytorch, Pandas, and Numpy to predict pump degradation with over 80% precision, leveraging time-series data to capture historical degradation patterns effectively. This predictive maintenance system has substantially reduced downtime and maintenance costs.Created a user-friendly web application integrating the LSTM model, which alerts staff via visual alarms about pump health, facilitating immediate maintenance actions. Developed the application’s frontend using HTML and CSS and the backend with Flask in Python and JavaScript.

Delivered personalised customer recommendations and diagnosed and resolved customer issues. My exceptional performance was recognised by the regional manager.Managed daily store operations individually and promptly addressed any in-store challenges or issues.

Analysed biomedical equipment maintenance data to ensure contractor compliance with timelines.Organised quarterly meetings with contractors and stakeholders to discuss maintenance progress and make plans to minimise downtime.Examined biomedical equipment to ensure compliance with international safety standards and conducted specification vetting for market inquiries

Performed on-site corrective and preventive maintenance for medical equipment, completing approximately 1,600 cases annually with 100% user satisfaction.Collaborated with the sales team on client-site installations and conducted demonstrations to facilitate customer understanding and engagement