43 deep-learning "Computer Vision Center" Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
. Required Qualifications: PhD in Computer Science, AI/ML, Computational Biology, or a related quantitative field. Proven expertise in deep generative modeling and large-scale multimodal learning. Experience
-
novel, tablet-based assessments of young learners’ curiosity, creativity, initiative, problem-solving, and scientific inquiry, which are skills that create a strong foundation for lifelong learning
-
join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
-
. Responsibilities may include: Designing and conducting studies on the clinical impact of GLP-1 and other metabolic therapies Developing and applying computer vision and machine learning techniques to analyze
-
scientists, and machine learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a
-
of the selected candidate, budget availability, and internal equity. The Fellow will teach one course per year in the Department of Religious Studies at Stanford, give one talk on their research during the term
-
patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
-
of urothelial exfoliation in cancer therapeutics. The labs are committed to fostering a highly collaborative and scientifically rigorous environment. This position offers an excellent opportunity to learn about
-
-based models as well as patient-derived xenograft models of liver cancer. This position is suitable for a highly motivated self-starter who excels in a dynamic environment offering varied learning
-
, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling