I am constantly looking for highly motivated/skilled students and postdoctoral researchers passionate for machine learning, information processing, and information theory.
Please email us to discuss possible research opportunities in the Information, Inference, and Machine Learning Lab at University College London.
Mobile health technology – encompassing mobile sensor, computation, communication, and user-interface capability – has the potential to support healthcare of an increasingly ageing population. It offers the opportunity to perform clinical diagnoses on the patient side, contributing to a more sustainable demand for healthcare systems in developed nations but also more widespread use of healthcare in developing ones. The overarching challenge relates to the development of high-accuracy, low-cost, portable mobile health applications capable of diagnosing a range of conditions prevalent in the older population.
This project will develop a Lab-on-App to non-invasively diagnose anaemia and its causes (e.g. genetics, diet, or injury) that can be easily used by older people, carers, or healthcare professionals. It involves the development of:
(1) Sensor technology capable of extracting information / images from the body or body fluids including (i) electrochemical sensors to measure concentration of urea on urine or (ii) multi-spectral sensors to measure skin / body fluids appearance
(2) Machine learning technology that delivers diagnoses of anaemia and its causes given the data collected by the aforementioned sensors.
(3) An android / ios application offering users an interface to collect data, analyse data, and deliver the diagnostics.
Applications for the studentship is open now, with an application deadline of 30 June 2021.https://www.ucl.ac.uk/electronic-electrical-engineering/phd-studentship-lab-app
iMIRACLI (innovative MachIne leaRning to constrain Aerosol-cloud CLimate Impacts) brings together leading climate and machine learning scientists across Europe with non-academic partners to educate a new generation of climate data scientists.
This EU funded Marie Skłodowska-Curie Innovative Training Network (ITN) will fund 15 PhD students (1 in our group) across Europe. They will develop maching learning solutions to deliver a breakthrough in climate research, by tracing and quantifying the impact of aerosol-cloud interactions from the microscale to large-scale climate.
Each student will have an interdisciplinary supervisory team, combining academic climate and machine learning supervisors as well as a non-academic advisor. International secondments to co-supervisors as well as to the non-academic partners will enrich student experience and training.
PhD students will begin their projects in September 2020, kicking off with a summer school held at Oxford.
Candidates must be Early-Stage Researchers (in the first four years of their research careers and not yet have been awarded a doctoral degree) and are required to undertake transnational mobility (move from one country to another).
Applications for studentships are open now, with an application deadline of 3 February 2020. All applications must be submitted through the iMIRACLI project webpage:www.imiracli.eu