08 Oct
Imperial College London
White City
Job details
Here’s how the job details align with your profile.
Pay
- £43,003 - £46,297 a year
Job type
- Full-time
Location
White City
Full job description
Reference Number
MED04855
Faculty / Department
Faculty of Medicine
Salary
£43,003 - £46,297 per annum
Location / Campus
White City Campus - Hybrid
Contract Type / Work Pattern
Full time - Fixed term
Closing date
22-Oct-2024
Join our research team as an AI Researcher in Spatial Epidemiology, funded by the Schmidt Sciences AI2050 grant.
About the role
Join our research team as an AI Researcher in Spatial Epidemiology, funded by the Schmidt Sciences AI2050 grant, to apply advanced AI techniques, such as deep generative modelling,
probabilistic programming and, potentially, geometric learning, to tackling critical challenges in spatial epidemiology. Contribute to innovative projects in disease mapping, spatial statistics, and population genetics while disseminating ground-breaking findings to both academic and policy-making audiences
What you would be doing
In this role you will leverage advanced AI techniques, such as deep generative modeling, to develop cutting-edge models in computational epidemiology, focusing on areas including but not limited to spatial statistics, population genetics and broader disease surveillance.
You will adopt existing and develop new computational statistical, machine learning and deep learning methods for analyzing spatiotemporal health data. Your work will include creating and disseminating replicable and reproducible computational workflows, as well as training practitioners in these new methods.
Additionally, you will communicate your research findings effectively to academic audiences through conferences and journal publications,
and to policymakers through national and international meetings. You will be responsible for submitting your research publications to peer-reviewed journals and actively seeking external research funding.
Collaboration is a key aspect of this role. You will work closely with various departments within the SPH (EBS + DIDE) and have the opportunity to interact with researchers from prestigious institutions such as Oxford, Copenhagen, Singapore, Switzerland, and South Africa whenever there is a suitable fit.
Relevant literature:
- "PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation." , Semenova, Xu, Howes, Rashid, Bhatt, Mishra, Flaxman. Journal of the Royal Society Interface 19, no. 191 (2022): 20220094.
- "PriorCVAE:
scalable MCMC parameter inference with Bayesian deep generative modelling." (2023), Elizaveta, Verma, Cairney-Leeming, Solin, Bhatt, Flaxman, arXiv:2304.04307.
- “Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya”. Semenova, Mishra, Bhatt, Flaxman, Unwin (2023). In International Workshop on Epistemic Uncertainty in Artificial Intelligence (pp. 13-27). Cham: Springer Nature Switzerland.
- “Federated learning for non-factorizable models using deep generative prior approximations” (2024), Hassan, Bon, Semenova, Mira, Mengersen, arXiv:2405.16055
What we are looking for
- Deisrable is practical experience with one or all of the (a) deep learning architectures, e.g. CNNs, GNNs, (b) deep generative models, (c) Bayesian inference.
- Expereince with deep learning frameworks such as PyTorch, Jax, TensorFlow
- Familiarity with probabilistic programming languages such as Stan, PyMC, NumPyro
- Expereince working with real-life data, with preference for spatiotemporal health data experience
- Experience with version control and GitHub
What we can offer you
This position comes with
- an opportunity to further develop your skills by using cutting-edge methods from rapidly evolving field of deep generative modelling, applying them to impactful real-life applications,
- funding for travel and conferences, along with access to a wide network of potential collaborators,
- the opportunity to continue your career at a world-leading institution,
- sector-leading salary and remuneration package (including 39 days off a year
Further information
This is a full time and a fixed term (10 months) role base at the White City Campus
If you require any further details on the role please contact: Dr Elizaveta Semenova – [email protected]
Available documents
Attached documents are available under links. Clicking a document link will initialize its download.
- Download: Employee Benefits Booklet.Pdf
- Download: Research Assistant Job Description.Pdf
Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.
We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications.
It is therefore advisable that you submit your application as early as possible to avoid disappointment.
If you encounter any technical issues while applying online, please don't hesitate to email us at [email protected]. We're here to help.
About Imperial
Welcome to Imperial, a global top ten university where scientific imagination leads to world-changing impact.
Join us and be part of something bigger. From global health to climate change, AI to business leadership, here at Imperial we navigate some of the world’s toughest challenges. Whatever your role, your contribution will have a lasting impact.
As a member of our vibrant community of 22,000 students and 8,000 staff, you’ll collaborate with passionate minds across nine London campuses and a global network.
This is your chance to help shape the future.
We hope you’ll join us at Imperial College London.
Our Culture
We work towards equality of opportunity, to eliminating discrimination, and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. You can read more about our commitment on our webpages.
Our values are at the root of everything we do and everyone in our community is expected to demonstrate respect, collaboration, excellence, integrity, and innovation.