Doctoral Student

We are seeking an outstanding, highly-motivated, curious and creative candidate for cutting-edge theoretical research addressing fundamental questions in evolution.

About the proposed research:

Plants that are both male and female, are prone to self-fertilization – the formation of offspring for which the very same plant is both the mother and father. As such self-fertilization offspring could suffer from genetic defects, plants have evolved specialized mechanisms called ′′self- incompatibility" to reduce self-fertilization.

These self-incompatibility mechanisms distinguish between “self” and “non-self” fertilization by specific matches and mismatches between pairs of specialized proteins, expressed at the male and female reproductive organs. We are studying theoretically the evolution of a particular self-incompatibility mechanism, found in common plants such as apple, pear and Petunia.

This research addresses fundamental questions regarding the joint evolution of multiple genes that should function together and hence could constrain each other’s evolution. The research includes stochastic simulations and theoretical aspects. We use a variety of tools, such as: mathematical modeling, stochastic computer simulations, and apply methods from statistical mechanics and information theory.

To read more please also check our recent publication: https://www.nature.com/articles/s41467-024-49163-7#citeas

About the lab:

Our lab is located in the Rehovot campus of the Hebrew University, and is part of the Plant Science and genetics Institute, Faculty of Agriculture.

The Faculty has a diverse student body of 2,300 students.

Lab website: https://www.friedlander-lab.net/

The project is co-supervised by Dr. Tamar Friedlander (Hebrew University, plant science and Genetics Institute. ) and by Prof. Ohad Noy Feldheim (Hebrew University, Einstein Institute Of Mathematics )

http://www.ma.huji.ac.il/~ohadfeld/

Qualifications

• Curiosity and keen interest in basic research
• Good programming skills (preferentially Matlab or Python)
• Strong research and data analysis skills
• Ability to learn and work independently.
• MSc degree in in either physics, mathematics, computer science, computational biology, engineering or another related field.
• Prior background in evolutionary biology or population genetics in an advantage (but not a must).

How to apply:

Interested candidates should send:

CV + transcripts + contacts of 2 references

Cover letter in which they briefly write why they are interested in this position and why they think they fit.

• Informal inquiries are also welcome.