-
Details Title Postdoctoral Fellowship Position in Visual Computing at Harvard University School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Sciences
-
Process: Interested candidates should submit a cover letter, CV, a brief research proposal, and two to three references’ names and contact information. Applications will be reviewed on a rolling basis until
-
for genome sequencing data to enhance our understanding of disease processes including cancer and brain-related diseases. Areas of interest include: Identification of mosaic mutations in non-tumor cells and
-
outstanding opportunities for continued scientific development and for contributing to pioneering research. Specifically, the fellow will apply as well as develop code to process and analyze mycobacterial
-
before the fellowship start date. Applicants with external funding are welcome to apply but must undergo the same review process. Additional Qualifications Special Instructions APPLICATION DEADLINE has
-
countervailing immunoediting processes that seek to control and eradicate these cancers. This project specifically focuses on ovarian cancer, a difficult-to-treat and life-threatening cancer for which early
-
. Cross-Disciplinary Fellowships (CDF) are for applicants with a PhD from outside the life sciences (e.g. in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in
-
neurodegenerative processes. Responsibilities: Conduct biochemical, genetic, and transcriptomic analyses of key regulators of brain aging using mouse and cerebral organoid models. Design and perform experiments
-
plasticity in the olfactory system. The olfactory tubercle is a ventral striatal brain region implicated in reward processing and motivated behaviors including addiction. Learning-induced changes in neural
-
for energy-efficient building and urban operation Exploration of novel Human-Building Interaction interfaces Design of cooperative energy-sharing systems across multiple buildings Integration of data-driven