Postdoctoral Fellow - Computational Analysis of Human Gene by Environment Interactions

European Molecular Biology Laboratory; Heidelberg; Vor Ort; EUR 55.000 - 85.000; Vollzeit

Zusammenfassung

An established industry player is seeking a highly motivated computational biologist to join a collaborative research initiative focused on the interplay between environment and genetics. This role involves working with large datasets and advanced computational techniques to uncover insights into human phenotypes. With a commitment to innovation and professional growth, this position offers a supportive environment that values diversity and encourages continuous learning. If you are passionate about leading scientific projects and making impactful contributions, this opportunity is perfect for you.

Leistungen

Flexible working

Medical insurance

Generous leave

Campus facilities

Family support

Professional development support

Qualifikationen

• PhD or equivalent in relevant fields with computational experience.
• Experience with statistical or machine learning techniques.

Aufgaben

• Work on a project linking exposure data with human genetic resources.
• Identify Gene x Environment interactions using large datasets.

Kenntnisse

Python

Bayesian inference

Deep learning

Statistical techniques

Ausbildung

PhD in Computer Science

PhD in Biological Sciences

Tools

PyTorch

Keras

TensorFlow

Pyro

Jobbeschreibung

We are seeking a highly motivated computational biologist to join the research groups of Oliver Stegle (EMBL Heidelberg) and Roel Vermeulen (Utrecht University) on an interdisciplinary project investigating the combined impact of environment and genetics on human phenotypes. Set within the Human Ecosystems Transversal Theme, the postdoctoral candidate will join a collaborative community advancing computational techniques for large human cohorts, focusing on the exposome-genome interface.

About the Human Ecosystems theme

This EMBL initiative bridges human cohort and molecular research to understand environmental impacts on phenotypes, utilizing advanced generative-transformer models for multi-disease risk prediction. Oliver Stegle’s group pioneers methods for deciphering molecular variation, while Utrecht’s IRAS specializes in environmental epidemiology and exposome science. The role involves working with large datasets linking genomic and exposomic data.

Your role

You will work across EMBL and Utrecht University on a project that links exposure data—covering physical-chemical, social, and dietary factors—with human genetic resources from biobanks like UKBioBank and Lifelines. Your goal is to identify Gene x Environment interactions and extend these insights to complex longitudinal data, building on existing generative transformer models to interpret genetics-to-phenotype pathways.

Requirements

1. PhD or equivalent in computer science, statistics, mathematics, physics, engineering, or biological sciences with computational experience.
2. Experience with statistical or machine learning techniques (e.g., Bayesian inference, deep learning).
3. Skills in Python and frameworks like PyTorch, Keras, Pyro, or TensorFlow.
4. Motivation to lead scientific projects and a supportive, creative team spirit.
5. Willingness to travel between Utrecht and Heidelberg.

Desirable

• Expertise in biological data science, genetic analysis, or related fields.
• Interest in learning for candidates with less biology background.

Additional information

• Position duration: 2-year contract, renewable up to 5 years.
• We value diversity and encourage applications regardless of background.
• Support for professional development through EMBL’s Career Service.

Why join us

Located on the Wellcome Genome Campus, EMBL offers a collaborative, inclusive environment with benefits such as flexible working, medical insurance, generous leave, campus facilities, and family support. We promote a culture of innovation and support your professional growth.

Application process

Apply by submitting a cover letter and CV via our online system before 01/05/2025. We respond within two weeks after the deadline.