Sort by
Refine Your Search
-
related to protein homology modeling, ligand docking and drug design to develop the next generation of safer anesthetics. Specifically, this individual will initially be an integral developer of a large
-
their mechanisms of infection and exploring their navel applications in tissue engineering and gene therapy. Key Responsibilities: Design and execute experiments to investigate AAV infection their replication
-
of decarbonized water and energy systems; and 3) supporting the design and enforcement of water-energy-food policies. We seek a new team member who enjoys team-science and engineering and would thrive
-
, clinical and molecular epidemiology, research design, and biostatistics with opportunity for training in Stanford’s Preventive Cardiology Clinic, Stanford Cancer Institute, and Department of Epidemiology and
-
to nominate cell designs that will enable engineering the next generation of CAR T cell immunotherapies. Applicants will have the opportunity to work with a stellar team of interdisciplinary scientists
-
emergency medicine, and have access to mentorship and infrastructure designed to support your growth as an independent investigator. This position includes both active participation in ongoing funded projects
-
or the interest in learning programming is highly desirable. A successful candidate will interact closely with computational biologists and be responsible for designing and executing experiments that will, in part
-
for ambitious science. Position type: Full‑time; open to candidates from recent PhD graduates to established senior researchers. Required Qualifications: • Design, execute, and analyse high‑density
-
computational and preclinical models informed by patient data. A successful candidate will leverage models of Treg suppression, Perturb-seq workflows, and receive assistance from a laboratory technician to design
-
experience building ML systems, designing and running experiments in PyTorch or JAX Strong publication record in top machine learning conferences (e.g. NeurIPS, ICML, ICLR). A strong background in theory is a