Strong expertise in Bayesian statistics and Gaussian processes.
Proven track record of research contributions in top-tier conferences or journals.
Deep understanding of machine learning fundamentals and core statistical principles.
Proficient in Python and PyTorch development.
Experience applying Bayesian methods in deep learning contexts.
Familiarity with topics like causal inference and model interpretability is advantageous.
Responsibilities
Design and implement innovative Bayesian statistical methods.
Convert machine learning research into production-ready implementations.
Establish robust frameworks for model evaluation.
Co-author research papers and abstracts to share findings.
Collaborate with engineers and scientists across disciplines.
Mentor junior team members to foster their growth.
Benefits
Opportunity to work on cutting-edge statistical methodologies.
Collaborative environment with interdisciplinary teams.
Chance to publish in prestigious conferences and journals.
Mentorship opportunities for professional growth.
Engagement in impactful research that drives innovation.
Full Job Description
Responsibilities
Design and implement novel Bayesian statistics methods.
Translate machine learning papers into production-ready code.
Build robust model evaluation frameworks.
Disseminate the results by co-authoring research papers and abstracts.
Collaborate with a multidisciplinary team of engineers and scientists.
Co-mentor junior members of the team.
Qualifications
PhD degree 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 will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics journals.
Excellent understanding of core machine learning concepts.
Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
Excellent skills in Python and PyTorch.
Experience with applying Bayesian statistics to uncertainty quantification in deep learning and model explainability.
Experience in deep learning. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability and computational pathology is a bonus.
