103 machine-learning "https:" "https:" "https:" "UCL" "UCL" research jobs at Stanford University
Sort by
Refine Your Search
-
Appointment Term: 1 year Appointment Start Date: TBD Group or Departmental Website: https://yangresearchlab.stanford.edu/ (link is external) https://med.stanford.edu/microimmuno.html (link is external) https
-
, typically gained through completion of an undergraduate degree in a related field. General computer skills and ability to quickly learn and master computer programs. Ability to work under deadlines with
-
, typically gained through completion of an undergraduate degree in a related field. General computer skills and ability to quickly learn and master computer programs. Ability to work under deadlines with
-
and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
-
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
-
: The appointment will be for one year. Appointment Start Date: 01/15/2026 or once the position is filled Group or Departmental Website: https://woods.stanford.edu/ (link is external) https://fieldlab.stanford.edu
-
standard study questionnaires and tests, score test measurements and questionnaires, and code data for computer entry. Perform quantitative review of forms, tests, and other measurements for completeness and
-
The Stanford Center on Early Childhood seeks a full time Senior Researcher to join our team. The Senior Researcher will work under the general direction of the Director of Learning and Evaluation to develop
-
to apply theoretical knowledge of science principles to problem solve work. Ability to maintain detailed records of experiments and outcomes. General computer skills and ability to quickly learn and master
-
a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make