- Developing causal generative models, which scale to large datasets of hundreds of thousands of samples and thousands of variables and can leverage existing biological prior knowledge, multi-omic data and perturbation information.
- Applying these methods to state-of-the-art single-cell perturbation datasets and/or population-scale cohorts, to dissect human disease variants and to gain insights into dynamic gene regulation in development, health and disease.
- Developing robust software solutions to enhance the utility of developed methods and to scale them to clinical applications.
- Contract duration: 2 years
- Grading: Stipend year 1
- Reference number: HD02583
- A PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical development
- Experience with implementing machine learning or statistical learning (e.g. Bayesian inference, deep-learning)
- Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro or TensorFlow
- The Motivation and Dedication to lead a scientific project and you are a supportive, creative and responsible team member.
- Expertise in biological data science and data-driven discovery, scientific programming and omics data analysis and genetics. We encourage applications also from candidates with little or no background in biology or medicine, and a keen interest to learn.
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Postdoctoral Researcher in Computational Genomics - Heidelberg, Deutschland - Embl-Ebi
Beschreibung
Your role
You will work in a team together with experimentalists and other computational
scientists to develop tailored machine learning and causal inference methodologies that are designed to fully exploit data from population scale cohorts and perturbation screens. We are currently designing machine-learning models for deciphering genomic variation, coupled with genetic perturbations in different model systems, including iPSC-derived models. The primary goal of this position is to develop dynamic, casual models of gene regulation and apply them in an interdisciplinary setting. Your activities will comprise:
The project setup allows for some flexibility according to qualifications and interests and we encourage you to apply even if you do not meet all, but some of the requirements below.
Closing date: 12 May 2024
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