Strong understanding of survival analysis methods.
Research-focused with impressive publication history in A* conferences or top-tier journals.
Solid grasp of core machine learning concepts.
Proficient in statistics, linear algebra, probability, and machine learning.
Skilled in Python and PyTorch.
Experience in advanced topics like self-supervised learning and causal inference is advantageous.
Responsibilities
Design and implement new survival analysis methods.
Translate machine learning research into production-ready code.
Build robust model evaluation frameworks.
Co-author research papers and abstracts to disseminate results.
Collaborate with a diverse team of engineers and scientists.
Co-mentor junior team members.
Benefits
Opportunities for professional growth and development.
Access to a multidisciplinary team and collaboration with experts.
Engagement with cutting-edge research in machine learning and statistics.
Chance to publish in reputable conferences and journals.
Supportive environment for mentoring and team collaboration.
Full Job Description
Responsibilities
Design and implement novel survival analysis 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 machine learning or statistics.
Excellent knowledge of survival analysis methods.
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 in deep learning. Experience in self-supervised learning, multi-modal learning, domain adaptation, causal inference, model interpretability and computational pathology is a bonus.
