Member of Technical Staff, Bayesian Statistics
Develops novel Bayesian statistical and machine learning methods, production implementations, and evaluation frameworks for clinical AI. The role requires a PhD in statistics or machine learning, strong Python and PyTorch skills, and expertise in Bayesian modeling and deep learning.
About the job
Responsibilities
- Design and implement novel Bayesian statistics methods.
- Translate machine learning papers into production-ready code.
- Build robust model evaluation frameworks.
- Disseminate results by co-authoring research papers and abstracts.
- Collaborate with a multidisciplinary team of engineers and scientists.
- Co-mentor junior team members.
Requirements
- PhD in statistics or machine learning.
- Excellent knowledge of Bayesian statistics, including Gaussian processes and Bayesian clinical trial design.
- Passion for research, attention to detail, and ability to drive tasks to completion.
- Strong preference for papers in A* conferences such as ICML, ICLR, NeurIPS, or CVPR, or in top-tier statistics journals.
- Excellent understanding of core machine learning concepts.
- Excellent knowledge of statistics, linear algebra, probability, and machine learning foundations.
- Excellent skills in Python and PyTorch.
- Experience applying Bayesian statistics to uncertainty quantification in deep learning and model explainability.
- Experience in deep learning.
Nice-to-haves
- Self-supervised learning.
- Survival analysis.
- Multimodal learning.
- Domain adaptation.
- Causal inference.
- Model interpretability.
- Computational pathology.
Skills
Bayesian Statistics, Gaussian Processes, Bayesian Clinical Trial Design, Machine Learning, Python, PyTorch, Deep Learning, Uncertainty Quantification, Model Explainability, Self-Supervised Learning, Survival Analysis, Multimodal Learning, Causal Inference, Model Interpretability, Computational Pathology
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