91 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including bioengineering, computer science, statistics, or mathematics. Strong
-
and attention to detail; Proven technical and analytical skills; Ability to troubleshoot an experiment as necessary. Proficiency with a computer; Knowledge of math and statistics, experience with PRISM
-
would include: Co-developing a hybrid machine learning/process-based model of anaerobic digestion processes Performing techno-economic and lifecycle analysis of microgrids build around novel biogas-fueled
-
clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating
-
the Education Data Science program) develop cultural competencies (via events organized by the Race, Inequality, and Language in Education program and the initiative on Learning Differences and the
-
one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
-
. She is also a faculty member in the Biophysics Program and a Faculty Fellow of the Sarafan ChEM-H (Chemistry, Engineering and Medicine for Human Health) Institute. Cegelski’s PhD in Chemistry and
-
. Developmental Cell. doi.org/10.1016/j.devcel.2021.07.009. Required Qualifications: A PhD in biology, genetics, development, neuroscience, or a related field Prior experience with iPS cell culture and
-
. 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
-
Cancer Registry, as part of the national SEER registries. The postdoc fellow will work closely with statisticians, computer scientists, oncologists, and epidemiologists in the lab and other collaborating