PhD Studentship: Machine Learning Models: The University of Surrey is offering a fully-funded PhD studentship in collaboration with the UK Animal and Plant Health Agency (APHA). This project focuses on developing advanced bioinformatic approaches, particularly machine learning models, for the surveillance of zoonotic diseases—those infectious diseases that can be transmitted between animals and humans. The successful candidate will work with a team to integrate DNA sequence data from APHA samples, predict pathogen characteristics, and aid in strategies for preventing and controlling infectious disease outbreaks.
Designation: PhD Student
Research Area: Machine Learning Models for Zoonotic Disease Surveillance
Location: University of Surrey, Guildford
Eligibility/Qualification:
- Open to candidates eligible for UK/home rate fees.
- Minimum entry requirements for the PhD program.
- Bachelor’s Degree (2:1 or higher) or equivalent in bioinformatics, data science, statistics, machine learning, artificial intelligence, computer science, mathematics, physics, biology, or a related field.
- Familiarity with programming, especially R or Python, is required.
- Consideration for candidates with relevant research experience.
Job Description:
PhD Studentship: Machine Learning Models for Zoonotic Disease Surveillance
The PhD student will:
- Develop computational methods to identify genetic signatures predicting pathogen characteristics.
- Utilize APHA’s data bank of DNA sequences to map the genetic makeup of pathogen outbreaks.
- Explore high-dimensional machine learning methods, Bayesian statistical approaches, and spatially resolved models for prediction.
- Address challenges in modeling complex interactions between genes for predicting pathogen behavior.
How to Apply: Interested candidates should submit their applications via the Biosciences and Medicine PhD program page. Instead of a research proposal, applicants should upload a document stating the title of the project and the name of the relevant supervisor (Dr Alexessander Couto Alves).
Interview Date: January 30, 2024, 9 am-12 noon
Funding: Fully funded for this project, covering tuition fees, a generous UKRI-level stipend for 3.5 years, and conference/training expenses.
Enquiries: Contact Dr Alexessander Couto Alves
Last Date for Apply: 24 January 2024
Disclaimer: This job post is sourced from a reliable reference. Applicants are advised to verify details from the official site for further information.
