Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
novel Hi-C library preparation to enable HGT to be tracked in situ in microbiomes. These will then be used to understand microbiome evolutionary dynamics at high-resolution in response to perturbations
-
) section. The BEE section investigates ecological and evolutionary patterns and processes underpinning biodiversity, scaling from genes to communities and ecosystems, and how these are affected by
-
ecological and evolutionary processes. Through the integration of paleontological, geological, geochemical, and biological data, the project aims to uncover how Earth’s systems, climate, and life have
-
Optics system. The primary science goals for GMTNIRS are the characterization of exoplanet atmospheres and stellar astrophysics at all evolutionary stages. The applicant would join the project in the final
-
with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk Project Description: The highly
-
genetics, evolutionary analysis, mammalian cell culture, and live-cell imaging. Through collaborations, we use cryo-electron microscopy and cryo-electron tomography for structural studies. For more
-
at https://postdoc.wustl.edu/prospective-postdocs-2/ . For info on the Mallott lab please visit https://mallott-lab.github.io . For info on the Gildner lab please visit https://www.reachresearch.org
-
Description The Arthropod Disease Vector Biology lab (https://www.imbb.forth.gr/en/research ), headed by Dr. Michail Kotsyfakis, invites applications for one research assistant position to work on the newly
-
. The successful candidate will hold an advanced degree in an area of Organismal or Evolutionary Biology; and undergraduate teaching experience in an introductory biology lab course for majors focusing on organismal
-
an emphasis on the development of methodologies and techniques for Evolutionary Computation and Machine Learning. Work plan: Review of the state of the art in Machine Learning and Deep Reinforcement Learning