PhD in machine learning, computer vision, or statistics.
Demonstrated passion for research and meticulous attention to detail.
Publications in top-tier conferences or journals preferred.
Strong grasp of core machine learning concepts.
Proficient in Python and PyTorch.
Background in deep learning and computational pathology, with bonus skills in advanced learning techniques.
Responsibilities
Design and deploy innovative machine learning models for computational pathology.
Convert academic research into operational code efficiently.
Establish comprehensive model evaluation systems and monitor outcomes.
Publish findings as a co-author of research papers and abstracts.
Work closely with a diverse team of engineers and scientists.
Guide and mentor junior team members.
Benefits
Collaborative multidisciplinary team environment.
Opportunities for co-authoring research publications.
Engagement in cutting-edge machine learning projects.
Career development through mentorship opportunities.
Full Job Description
Responsibilities
Design and implement novel machine learning models and methods for computational pathology.
Translate machine learning and statistics papers into production-ready code.
Build robust model evaluation frameworks and monitor model performance.
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, computer vision or statistics.
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 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 and computational pathology. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference or model interpretability is a bonus.
