Full time fixed term contract for 12 months in the first instance, with possibility of extension An exciting opportunity in a newly created role within the team focused on wearables and health within the Charles Perkins Centre and the Faculty of Medicine and Health Base Salary ranging from $104,633 to $113,992 + 17% superannuation About the opportunity The University of Sydney, Charles Perkins Centre - Since our inception 160 years ago, the University of Sydney has led to improve the world around us. We believe in education for all, and that effective leadership makes lives better. These same values are reflected in our approach to diversity and inclusion and underpin our long-term strategy for growth. We are Australia's first university and have an outstanding global reputation for academic and research excellence. Across our campuses, we employ over 8,100 academic and non-academic staff who support over 73,000 students.
Charles Perkins Centre is at the forefront of multidisciplinary research and education and undertakes high-impact research that anticipates the cardiometabolic health issues of tomorrow while overcoming the challenges of today.
We are seeking to employ an enthusiastic, talented, and passionate Data Scientist (HEO 7) to provide technical support and leadership to the research team of Professor Emmanuel Stamatakis.
This is an excellent opportunity for an outstanding Senior Data Scientist to develop a strong career in health-related research and gain substantial experience and receive training on wearables-based measurement of physical activity and sleep. The position is located at Charles Perkins Centre, a leading multidisciplinary research institute of the University of Sydney, the incumbent will support the activities within the newly founded Mackenzie Research Wearables Hub at Charles Perkins Centre.
Your key responsibilities will be: Analyse and process multimodal sensor data collected from wearables such as ECG, altimeter, accelerometer, gyroscope, polysomnography (PSG), and EMG.
Work with data from wearables worn on various body locations (wrist, thigh, chest, head), including nanopolymer technology.
Apply machine learning techniques such as supervised, semi-supervised, deep learning, reinforcement learning, transfer learning etc. to enhance activity recognition.
Collaborate with research teams across multiple studies to ensure accurate data collection, labelling, and quality control.
Manage large datasets and conduct data preprocessing, feature extraction, and model validation using appropriate statistical and computational methods.
Develop and refine signal processing algorithms to handle data from complex wearables, including ECG, EEG, and EMG signals.
Implement and optimize activity classification algorithms for highly variable activities, including sleep stages, daily activities, and specialized movements (e.g., stair climbing, gym workouts).
Perform cross-validation, error analysis, and model tuning to ensure high accuracy in real-world applications.
Stay updated on the latest developments in wearable technology and machine learning to improve current methodologies and propose innovative solutions.
About you To be successful in this role, you will have: Honors or master's degree in data science, with relevant experience in a related field, or degree in a related area and substantial experience as research assistant/research officer.
Experience working in a research-intensive environment.
Strong background in machine learning (e.g., classification, supervised and semi-supervised learning, transfer learning) and signal processing.
Expertise in working with wearable sensor data such as ECG, accelerometers, gyroscopes, PSG, EMG.
Proficiency in Python, R, or other relevant programming languages.
Experience with tools for time-series analysis and signal processing (e.g., scikit-learn, TensorFlow).
Experience developing models using multimodal sensor data from wearables placed on different body locations.
Strong understanding of biomedical signal processing and familiarity with bioinformatics tools.
Knowledge of handling and analysing large datasets, with experience in data wrangling, data visualization, and feature engineering.
Good inter-personal skills and capacity to resolve challenging situations in the most professional manner.
Familiarity with nanopolymer technology in wearables would be desirable.
Possess a capacity to work against deadlines in a fast-paced and vibrant research environment.
Willingness and capacity to occasionally (e.g. 1-2 times per month on average) work out of regular office hours to accommodate the multiple time zones of the Team's international collaborators.
How to apply Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page.
For employees of the University or contingent workers, please login into your Workday account and navigate to the Career icon on your Dashboard. Click on USYD Find Jobs and apply.
For a confidential discussion about the role, or if you require reasonable adjustment or any documents in alternate formats, please contact Rachel Yazigi Recruitment Operations by email to ******. Applications Close Sunday 01 December 2024 11:59 PM
#J-18808-Ljbffr