91ÑÇÉ«

Position OverviewÌý

We are seeking a highly motivated Graduate Research Assistant with strong expertise in machine learning (ML) and language learning models (LLMs) to join our interdisciplinary research team at the Global & Environmental Health Lab, 91ÑÇÉ«. The successful candidate will contribute to an innovative project examining how extreme temperatures and exposure to poisonous gases influence the risk and progression of cardiometabolic conditions, functional disabilities, cognitive impairments, and visual impairments among older adults. 

This position will provide the opportunity to work at the intersection of climate change, environmental epidemiology, data science, and aging research, while developing advanced computational approaches to generate impactful insights for public health policy and interventions. 

Key Responsibilities 

Data Management & AnalysisÌý

• Acquire, clean, and manage large-scale datasets, including health, demographic, and environmental exposure data.Ìý

• Apply machine learning algorithms to model associations between environmental exposures (extreme temperatures, poisonous gases) and multiple health outcomes.Ìý

• Leverage natural language processing and language models to synthesize literature, identify knowledge gaps, and support model interpretation.Ìý

Research & Methodological InnovationÌý

• Develop and test predictive models to identify vulnerable subpopulations of older adults.Ìý

• Integrate multimodal data sources (health surveys, clinical data, satellite-based exposure data, etc.).Ìý

• Contribute to methodological advancements in explainable AI for health and environmental sciences.Ìý

Collaboration & DisseminationÌý

• Work closely with faculty, postdoctoral fellows, and interdisciplinary collaborators (e.g., public health, computer science, medicine, environmental sciences).Ìý

• Contribute to drafting research manuscripts, technical reports, and conference presentations.Ìý

• Support grant-writing efforts and assist with project administration when needed.Ìý

Qualifications 

Required: 

• Enrollment in (or recent completion of) a graduate program in Computer Science, Engineering, Data Science, Public Health, Epidemiology, Biostatistics, Environmental Health, or a related field.Ìý

• Demonstrated experience with machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).Ìý

• Proficiency in programming languages such as Python or R.Ìý

• Familiarity with large datasets and statistical modelling.Ìý

• Strong writing and communication skills.Ìý

Preferred: 

• Experience with language models (e.g., transformers, LLM fine-tuning, NLP pipelines).Ìý

• Knowledge of environmental epidemiology o°ù aging/gerontology research.Ìý

• Skills in geospatial analysis (e.g., satellite or sensor-based exposure data).Ìý

• Experience with cloud computing environments (AWS, GCP, or similar).Ìý

• Publication record or experience contributing to peer-reviewed manuscripts.Ìý

Professional Development Opportunities 

• Gain hands-on experience with advanced AI/ML applications in health and climate research.Ìý

• Work in a collaborative, interdisciplinary team with opportunities for co-authorship.Ìý

• Access to professional development workshops, mentorship, and training in cutting-edge computational methods.Ìý

Application Instructions 

Interested applicants should submit the following: 

1. A cover letter outlining relevant skills and research interests.Ìý

2. A CV or résumé.Ìý

3. Contact information for two references.Ìý

4. A writing sampleÌý

Applications will be reviewed on a rolling basis until the position is filled.Ìý

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