Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming (Internship) King Abdullah University of Science and Technology (KAUST)

Overview

For example, mature cells can be reprogrammed into pluripotent stem cells, and exciting work on engineered design of tissues and organs (organoids) are underway. On the one hand the community has since the sequencing of the human genome produced very efficient tools to read off the corresponding molecular events accompanying reprogramming and engineering of cells. 

Key Facts

Recently, the discovery of the CRISPR techniques has equipped us with unprecedented opportunities for precise writing or editing of the genomes. These developments in fundamental biology and biotechnology are currently opening new tools and perspectives of vital significance for drug development, regenerative medicine, synthetic biology, and personalized medicine. 

Yet, in essence all these efforts require and would be greatly facilitated if we could advance from correlative data-analysis to a predictive discovery of which interventions (edits, engineering) are producing which effects. 

Thus, we are facing the fundamental problem on how to discover causal relations from data, or in other words, can we derive quantitative predictive laws from data? They offer internships for several highly motivated bachelor (B.Sc.) or master (M.Sc.) students who will explore this fundamental question primarily from a computational standpoint. This includes using high-performance simulations of dynamical models, and design of algorithms in a controlled in-silico environment. Join this  Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming (Internship) project from King Abdullah University of Science and Technology (KAUST) .