Sort by
Refine Your Search
-
genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
-
who are unable to upload unofficial transcripts may send official transcripts to Politics Postdoc Search, Department of Politics, 001 Fisher Hall, Princeton University, Princeton, NJ 08540. A PhD is
-
-Sigler Institute for Integrative Genomics and the Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning
-
patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics of squamate reptiles; the largest group
-
fields. Candidate must have excellent computational and bioinformatic skills; abilities for developing simulation models will be highly valued; experience with ancient DNA genomic datasets is encouraged
-
the following areas as demonstrated through at least one first-author publication: computational biology/bioinformatics, cheminformatics, analytical chemistry/mass spectrometry/metabolomics, or machine
-
the applicant: *Cover letter *Curriculum vitae *Transcripts *Research Proposal indicating plans for two-year postdoc (maximum 5 pages double-spaced) *Dissertation abstract (including Table of Contents) *Writing
-
be sent to amferris@princeton.edu with the subject line "Ferris Lab Postdoc Inquiry 2025". Applications will be reviewed on a rolling basis, until the position is filled, with a final deadline of
-
"Ferris Lab Postdoc Inquiry 2025". Applications will be reviewed on a rolling basis, until the position is filled. Expected Salary Range: 65000-70000 The University considers factors such as (but not
-
areas as demonstrated through at least one first-author publication: computational biology/bioinformatics, cheminformatics, analytical chemistry/mass spectrometry/metabolomics, or machine learning