[PET] PhD positions in Digital Health, including Privacy-Preserving Analytics on Health Data

Emiliano De Cristofaro me at emilianodc.com
Thu Jun 30 20:10:58 BST 2016


UCL's Institute of Digital Health is currently offering 2 PhD studentships
to conduct research in the area of digital health. Potential supervisors
with an interest in Digital Health are listed at
https://iris.ucl.ac.uk/iris/browse/researchTheme/85. Example projects are
listed at https://www.ucl.ac.uk/digital-health/doctoral-research/index .

Details:
. Apply at https://www.prism.ucl.ac.uk/#!/?project=189
. Closing date Friday 22nd July 2016 (Interviews will be held 8-10 August)
. Studentship comprises fees and a tax-free stipend of £16,851 per annum
. Eligibility restricted to UK/EU citizens


Project Ideas:

1. User-centered, privacy-friendly analytics for health data
(Supervisory team: Emiliano De Cristofaro, Mirco  Musolesi, Angela Sasse)
In this project, we will investigate user-centric approaches for
aggregating and regulating access to health data, including sensory data
collected by apps and devices, as well as health-related information that
can be inferred from, e.g., browsing and search activities. We aim to
support usable, meaningful systems enabling users to visualize and use this
data as well as making it available, in a privacy-respecting way, to third
parties, including researchers, service providers, and health professionals.

2. Personalizing treatment of complex disease from large molecular and
clinical datasets
(Principal supervisor: Prof. Natasa Przulj)
The wealth of the available clinical and molecular data offers an
unprecedented opportunity to answer key questions about causes, variation
and treatment. However, analysis of these complex, heterogeneous data is
computationally hard.  The project will introduce computational advances in
algorithms for data analysis and fusion that would enable use of all
available data towards answering fundamental medical questions.

3. Personal Digital Assistants in Digital Health
(Supervisory team: Philip Treleaven, Geraint Rees, Alan Payne)
The wealth of the available clinical and molecular data offers an
unprecedented opportunity to answer key questions about causes, variation
and treatment. However, analysis of these complex, heterogeneous data is
computationally hard. The project will introduce computational advances in
algorithms for data analysis and fusion that would enable use of all
available data towards answering fundamental medical questions.

4. Understanding and supporting medication adherence
(Supervisory team: Ann Blandford, Rob Horne)
There are many reasons (including not believing they help, unpleasant
side-effects, and just forgetting) for people not adhering to a prescribed
medication regime. But words like "adherence" assume a particular
relationship between clinician and patient. In this project, we will
explore alternative models of medication management and digital
technologies to support those models, drawing on human factors and
behavioural medicine.
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