Research Associate in Biostatistics and Machine Learning: Imperial College London is offering a scholarship opportunity for a Research Associate in Biostatistics and Machine Learning as part of a newly-funded BHF Professorship in Cardiovascular AI. This position aims to develop innovative algorithms for heart disease risk prediction using advanced statistical and machine learning techniques applied to high-dimensional clinical datasets.
Designation
Research Associate in Biostatistics and Machine Learning
| Attribute | Details |
|---|---|
| Research Area | Biostatistics, Machine Learning, Cardiovascular AI |
| Location | Hammersmith Campus, Imperial College London |
| Eligibility/Qualification | Strong background in biostatistical modeling; Excellent coding skills in R and Python; Prior experience with machine learning algorithms preferred; Track record of published research outputs. |
Description
The successful candidate will engage in developing novel algorithms for time-to-event analyses, high-dimensional data clustering, and causal inference. This role involves creating solutions to complex biomedical challenges that leverage large-scale imaging, genomic datasets, and advanced analytics. Experience with high-performance computing, particularly using the DNAnexus platform, will be a valuable asset.
Key Responsibilities
- Develop and test algorithms in the context of cardiovascular research.
- Collaborate within a multidisciplinary team to produce innovative solutions.
- Publish research findings and contribute to ongoing projects in cardiovascular AI.
How to Apply
Interested candidates should submit their applications through the Imperial College London job portal. Early application is encouraged as the role may close before the stated date due to high application volumes. For any technical issues during application, please contact support.jobs@imperial.ac.uk.
Last Date to Apply
December 10, 2024
This opportunity is an excellent chance for researchers in the field of biostatistics and machine learning to contribute to important work in cardiovascular health while advancing their careers in an esteemed academic environment.
