PhD Scholarship: Deconvolving Individual Bioaerosol Signatures Using AI-Driven Analysis of Complex Biological Datasets
Core
Develop an AI-based framework to identify individual-specific bioaerosol signatures from complex biological data within shared indoor spaces.
Role type
PhD Researcher (AI/ML for Bioaerosol Analysis)
Builds
AI-driven signal deconvolution framework for environmental monitoring
Domain
Environmental monitoring, bioaerosol analysis, artificial intelligence
Deliverable
research
Required skills
Python, machine learning, AI platforms (TensorFlow, PyTorch, scikit-learn), environmental aerosol sampling technologies, high-resolution chemical analysis
Preferred skills
nanotechnologies, material science, surface chemistry, bio- and chemical-sensing techniques, interferometric sensing
Technologies
TensorFlow, PyTorch, scikit-learn
Responsibilities
Integrate environmental aerosol sampling technologies with high-resolution chemical analysis; develop and refine AI-based framework for identifying individual-specific bioaerosol signatures; contribute to multidisciplinary research programme
Seniority
PhD Candidate