05 Postdoctoral Fellowship at European Molecular Biology Laboratory (EMBL), Germany

You will work on direct linkage across a diverse set of human traits with detailed healthcare, genetic and additional information in multiple human cohorts, and as such we are looking for an experienced post-doctoral fellow with a strong interest in health-related data science and proven expertise in modern AI techniques.

You will lead on a project funded by the EMBL Human Ecosystems transversal theme programme to investigate gene x drug exposure x phenotype relationships at scale over continuous time in large human cohorts. The successful candidate will join the Birney research team at EMBL-EBI working closely with Open Targets to apply and further develop an AI framework based on generative transformers (Delphi) for multi-disease and multi-drug modelling across continuous time in human populations. Some of the initial work will include incorporating prescription exposome data into Delphi and developing a pharmacogenetic analysis model, correlating genetic variation with exposure to drug outcomes based upon known associations and exploring novel genetic-drug exposure-phenotype associations.

Drug exposure is a strong environmental modifier and is intrinsically linked to disease risk and outcome in human populations. There remains many open questions and undiscovered interactions between drug exposure, genetics and disease onset/outcome that can be investigated using large human cohorts with detailed health records and genetics, providing the candidate with lots of research opportunities and potential novel findingsOne key factor which makes this type of research more powerful is recent innovations in generative AI making it possible to model all disease and other important factors, such as drug exposure and genetics at the same time, mapped to the same internal space (embedding) and across continuous time. This not only allows us to assess the overall impact of genetics and exposures on disease risk across a population but also provides a framework for assessing at which time across a life course these effects most strongly manifest.

We seek an ERC-funded postdoctoral scientist to investigate the mechanistic function of chromatin modifications in somatic and/or germ cells. The successful candidate will work on a cutting-edge experimental research project using high-throughput perturbation screening, epigenetic editing, multi-omics, and organoids. The objective is to uncover the regulatory logic by which distinct chromatin states shape gene expression patterns in health and disease, and the underlying mechanisms. You will enjoy substantial freedom (if desired) to follow curiosity-driven hypotheses, based on novel screening datasets.

This postdoctoral position is funded by OSCARS, a Horizon Europe project that fosters the uptake of Open Science in Europe by consolidating the achievements of world-class European RIs in the ESFRI roadmap and beyond into lasting interdisciplinary FAIR data services and working practices across scientific disciplines and communities (https://oscars-project.eu/about-oscars). The successful candidate will be the key actor of the LabID-PROV project (https://www.oscars-project.eu/projects/labid-prov-tracking-and-sharing-data-provenance-ro-crate-lab-integrated-data).

Lab Integrated Data (LabID, https://gbcs.embl.de/labid), is a web-based integrated platform for research data management featuring sample and dataset management, an inventory management system and an electronic lab notebook. It is designed to help individual scientists, research groups and core facilities better manage, annotate and share their experiments, assays, samples and datasets actively according to FAIR principles. While in LabID processed data can already be stored and connected to its primary data, associated assays (e.g. sequencing, light or electron microscopy) and original samples, accurate modelling of both Workflow (WF) and Workflow Run (an object modelling the execution a WF and gathering input & outputs datasets as well as the execution metadata e.g. parameters, configurations…) is currently lacking. In this project, we proposed to extend the LabID data model to include these concepts, offering a unified application to manage derived data provenance independently of analysis procedure and platform, and providing a concrete solution to ensure the traceability of derived data. To this end, we will use and integrate with several EOSC-Life resources e.g. WorkflowHub, RO-Crate, Galaxy, Zenodo and Workflow Run RO-Crate profiles to streamline derived data import and export.

We are seeking a highly motivated computational biologist to join the research group of Peer Bork within the Molecular Systems Biology Unit as a postdoctoral fellow. This bioinformatics group works on a wide range of topics with a current focus on microbiome analysis in ecosystems like the human gut and the ocean. Within the TREC (Traversing European Coastlines) project, we have gathered more than 3000 soil, sediment and water samples, and are currently analysing the resulting microbiome and multi-omics data. Additional data have been gathered within the EU-funded BIOcean5D project, where we are analysing global biodiversity patterns, and in our efforts to integrate public metagenomic data (e.g. in the SPIRE database) and the corresponding contextual data.