Sort by
Refine Your Search
-
Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
-
a PhD in machine learning, math, stats, physics, or some other technical area by the time the position starts. Additional Qualifications Candidates should have significant experience in some area of
-
position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
-
integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute to the writing of grants and
-
technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance
-
What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
-
about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Develop DNA-based sensors that seed crisscross assembly of single
-
with real-world samples, and effectively integrating them with microfluidics as a standalone device. This is a great opportunity to learn new skills, contribute to assay development, and intellectually
-
: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Design nucleic-acid nanostructures and assemble them in a wet laboratory
-
solver who wants to be part of a dynamic team. Information about the Church Lab: Learn more about the innovative work led by Dr. George Church here: https://churchlab.hms.harvard.edu/ , https