Sort by
Refine Your Search
-
knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
-
. We are seeking highly motivated individuals who are passionate about science, learning and having fun. Candidates with a strong background in molecular and cellular biology, and/or experience in
-
clinical or behavioral research, particularly with children and families Motivation to learn state-of-the-art methods and approaches for clinical trials Enthusiasm to improve the health of children and
-
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
-
research-practice partnerships and collaborations with community organizations. These partnerships provide fellows with opportunities to learn to collaborate with practitioners and policymakers to identify
-
-based role based at Stanford University’s Environmental Measurements Laboratory May require ~20% travel to train others on analytical approaches and learn new approaches at other laboratories (domestic
-
in the U.S. There will also be extensive opportunities to learn more about and work with Census-held administrative records. The successful candidate will have strong data science skills, including
-
to) the qualifications of the selected candidate, budget availability, and internal equity. Fellows are required to be in residence in the Stanford area during the appointment period, to teach one course during the
-
are an interdisciplinary research team that integrates single-cell and spatial genomics, lineage tracing, synaptic proteomics, functional perturbation screening, and machine learning to investigate how the human brain
-
and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning