79 parallel-and-distributed-computing-phd Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
common equipment, and also have the benefit of access to research facilities at Stanford University including core computing, microscopy, library, biostores, and analytical facilities. The Spin lab has
-
Postdoctoral (E3) Fellowship Program trains the next generation of scholars to conduct research toward equitable, impactful, and sustainable early childhood care and education systems. Why the E3 fellowship
-
. 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
-
scientists. Candidates with experience in prototyping, optical instrumentation, image processing, or translational device development are particularly encouraged to apply. Required Qualifications: PhD in
-
transplantation and to translate this knowledge into improved treatments. Required Qualifications: Individuals with a recent MD or PhD degree with skills in computational biology, bioinformatics, biostatistics
-
Postdoctoral position in Computational Immunology We are looking for two motivated postdoctoral researchers to work on human macrophage biology in the Department of Pathology at Stanford. Successful candidates
-
be distributed as open-source software to ensure reproducibility and transparency as well as supporting the extension of our approach to new domains. Required Qualifications: Doctoral degree Excellent
-
available immediately in the Molecular Imaging Program at Stanford (MIPS). The successful candidates will join a dynamic research group focusing on the development of peptide-based therapeutics and
-
learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high‑performance computing cluster offer an unmatched playground
-
. 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