A cure for Parkinson's disease has been held back for decades by the extreme difficulty of measuring whether proposed new drugs actually improve the patient's symptoms and daily life. TORUS aims to solve this through a novel platform of sensing technologies for use in patients' own homes along with an advanced data fusion and machine learning pipeline that measures changes in specific mobility-related behaviours over time.
Requirements
- Excellent and broad conceptual and practical knowledge of the state of the art in machine learning and data science.
- Demonstrably excellent software development skills with Python and relevant libraries (scikit-learn, PyTorch/TensorFlow, and others).
- Proven deep understanding of and demonstrable skills in data-driven experiments and performance evaluation.
- Track record in time series analysis or activity recognition for healthcare.
- Interest in health applications of data science.
Responsibilities
- Perform fundamental and applied research, within the TORUS data fusion and distributed machine learning team, in the key project area of human-behaviour analysis using spatiotemporal data from different sources and modalities.
- Contribute to the design of the TORUS computational architecture, including the planning of data collection activities with patients.
- Implement these procedures and techniques in the TORUS system.
- Publish the work in top journals and international conferences.
- Present the work in seminars and workshops as appropriate.
- Advise on and participate in deployment activities related to the TORUS system.
Other
- PhD in machine learning (or working towards one for Grade I), data science, artificial intelligence or related research area.
- First-authored publications on relevant topics in top-tier international conferences and workshops.
- Excellent communication and presentation skills.
- Proven ability to work both independently and as part of a team (TORUS is a very large project and team working will be a daily activity).
- Proven ability to plan, manage and prioritise own workload while responding to the changing priorities and needs of a busy, dynamic environment.
