Using data and network science approaches for the detection of food insecurity for patients with chronic health conditions


   Network Science


About the Project

In 2023, NetSI established a new institute hub in London, UK at Northeastern University London. The new NetSI in London was created to advance research, education, and innovation centred around network science. The hub in London will also facilitate the development and growth of collaborations and partnerships within the UK and across Europe and US. The London hub, along with others in the US, is part of the NetSI Global program which is dedicated to engaging and integrating regional network science communities andpartnering with regional talent, institutions, ecosystems of innovation.

The PhD student, even though based in London, will be a member of the Network Science Institute (NetSI), which has offices in Boston MA, Portland MA, and London UK. NetSI, founded in 2014, has emerged as a world leader of multidisciplinary research communities in the field of network science. NetSI brings together expertise from diverse disciplines, from the physical, information, and social sciences, with the goal to build and expand common, synthesising methodology and theory of networks, and to apply these tools to important societal challenges.

The successful doctoral student will be connected to NetSI’s vibrant community, with dozens of externally funded research programs, diverse faculty, and a uniquely skilled pool of fellow doctoral students and postdoctoral researchers across the global network.

The Project

We are seeking a PhD candidate to investigate the complex intersection between low-income patient cohorts and chronic metabolic conditions, such as obesity, hypertension, and diabetes, within the context of social determinants of health (SDOH) and nutrition. This research will explore the multifaceted nature of nutrition as a health-related social need, considering its economic and social determinants and their interrelationships. The project aims to address existing health inequalities and challenges in health communication and patient intake processes, which may introduce bias in differential diagnoses and disease classification.

As part of the project, we aim to develop a machine learning pipeline that will be able to bring together multi-modal data from the Electronic Health Record with multi-dimensional residence-related SDOH data from publicly available sources, to improve classification of food insecurity. Additionally, the study will examine the impact of resource constraints on preventative interventions and health outcomes for patients recommended diet and lifestyle changes. The candidate will contribute to understanding the evolving landscape of healthcare delivery, particularly the integration of SDOH screening and partnership models of care between healthcare clinics and Community-Based Organizations (CBOs), with data both from the USA and Europe. This research aligns with recent healthcare policies and initiatives, offering an opportunity to advance knowledge in the field and contribute to improving healthcare outcomes for vulnerable populations.

The ideal candidate has:

  • Bachelor’s degree (first/upper second or equivalent) in Complex Systems, Network Science, Data Science, Computational Social Science, Physics, Mathematics, Computer Science, or related subjects (essential).
  • Proficiency in programming languages relevant to research,e.g., Python, R, SQL, Julia (essential).
  • Fluency in English, both spoken and written (essential)
  • Highly collaborative spirit, personal initiative, and genuine interest in interdisciplinary teamwork (essential).
  • Excellent personal and good communication skills (essential).
  • An inquiring mind and the desire to challenge themselves (essential).
  • Enthusiasm for interdisciplinary applied research (essential).
  • Experience of, or willingness to learn, data visualisation techniques, and vectorial image design for presentation and project websites (essential).

The successful candidates will benefit from a brand-new campus on the banks of the river Thames next to Tower Bridge. This is an interdisciplinary, vibrant research environment with international collaboration and networking opportunities and dedicated research space. It will form the hub of a highly experienced, multi-institution supervisory team from NU London, Northeastern University, and the University of Kent. In addition, successful candidates will benefit from the unique connection to the wider Northeastern University network in North America, providing a range of additional research opportunities and learning resources.

Shortlisted candidates will be interviewed in July 2024. Candidates are welcome to contact the NU London supervisor with informal enquiries before the application deadline:

Funding provider: Northeastern University London (NU London)

Subject areas: Complex Systems, Computational Social Science, Network Science, Data Science, Physics.

Project start date: 1 October 2024

Supervisors (*lead):

  • Istvan Kiss* (Network Science Institute, Northeastern University London)
  • John Lowery (Bouve College, Northeastern University)
  • Riccardo Di Clemente (Complex Connections Lab, Network Science Institute, Northeastern University London)
  • Mark Wass (University of Kent)

Aligned programme of study: PhD in Network Science Mode of study: Full-time

Eligibility

Bachelor's degree in a relevant subject - 2:1 or 1st (essential)

  • Master’s degree in a relevant subject (optional)
  • English Language requirements:

If applicable – IELTS 7 overall (with a score of at least 6.5 in each individual component) or equivalent.

Nationality

Applications are open to UK and international students. Please indicate if you are likely to require a visa on your application. We are unable to support visa costs.

International travel

Students will have the opportunity to optionally travel to Northeastern University in North America to further their research training and experience.

How to Apply

Please send a CV and a Covering Letter stating how you meet the requirements and why you are interested in the proposed research project by clicking on this link by 23:59 on the 30th of June 2024. Please reference your application “PHDCH0524”


Computer Science (8) Physics (29)

Funding Notes

This scholarship covers the full cost of tuition fees and provides an annual stipend, including London allowance (set at UKRI rates), for 3.5 years. For the 2024/2025 academic year the total annual stipend is £21,237. Annual increments will be in line with UKRI rates.


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