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

This postdoctoral position is part of the EU cofund research project AMBER, Advanced Multiscale Biological imaging using European Research infrastructures, will address scientific and sectoral gaps in biological imaging ranging from molecular, through cellular, to tissue, organ and organism levels of organisation, and is coordinated by LINXS Institute of advanced Neutron and X-ray Science. AMBER is funded by the EU Marie Skłodowska-Curie (MSCA) COFUND scheme.

Your work may include clinical and biomedical projects. It may also include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical, biological, and methodological capabilities, with a profound potential impact for Europe’s next generation of research and researchers. When you have completed the AMBER programme you will be extraordinarily well equipped to further your career in academia, at infrastructures, in the health and MedTech sectors, and beyond.

EMBL is Europe’s life sciences laboratory – an intergovernmental organisation with more than 110 independent research groups and service teams covering the spectrum of molecular biology. It operates across six sites in Heidelberg (headquarters), Barcelona, Cambridge, Grenoble, Hamburg and Rome. Our mission is to perform basic research in molecular biology; train scientists, students and visitors at all levels; offer vital services to scientists in the public and private sectors within the member states; develop new instruments and methods; and engage actively in technology transfer.

The Steinmetz group at EMBL Heidelberg is seeking an ambitious postdoctoral fellow with interest in cardiomyopathies and genome biology. We work on novel strategies to treat cardiomyopathies and to understand the underlying disease and drug mechanisms. Our approach is based on cutting-edge functional genomics assays and single-cell technologies in combination with complex 2D and 3D disease model systems. The successful candidate will be able to work within an established team of scientists, research technicians, and a strong network of collaborators from academia and industry

Despite the emergence of AlphaFold and related AI-based modeling tools, large macromolecular complexes still cannot be consistently modeled with high accuracy. Experimental data can guide the modeling of large complexes; however, methods for modeling based on experimental data are not sufficiently automated, computationally inefficient, and lack general applicability. To address this, you will:

• Develop computational methods to model the atomic structure of large macromolecular complexes using tools such as AlphaFold, RoseTTAfold, and OpenFold, alongside your own innovative AI-based algorithms, and integrating experimental data from in-cell cryo-ET tomography and crosslinking mass spectrometry
• Investigate and implement methods that combine statistical techniques like Bayesian inference and Monte Carlo sampling with non-linear optimization approaches and advanced deep learning (e.g., geometric deep learning and reinforcement learning).
• Contribute to open-source codebases such as OpenFold, AlphaPulldown, and Assembline
• Disseminate your code in well-organized, documented, and rigorously tested software packages
• Use these new methods for research projects within the TransFORM consortium.

You will be affiliated with the research group of Jan Kosinski at EMBL Hamburg, but you will spend at least a year as a visiting postdoctoral fellow at the Fabian Theis group at Helmholtz Münich.

• Develop algorithms to identify macromolecular complexes in cellular tomograms, using image processing techniques such as template matching and machine learning
• Investigate and implement methods to enhance the speed and accuracy of particle identification by increasing algorithm efficiency, refining scoring functions, and applying the latest deep learning techniques in computer vision
• Explore the integration of spatial constraints from other data sources to improve particle identification
• Co-develop PyTME (https://github.com/KosinskiLab/pyTME)
• Disseminate your code in well-organized, documented, and rigorously tested software packages
• Use these new methods for research projects within the TransFORM consortium

The European Molecular Biology Laboratory (EMBL) is one of the highest ranked scientific research organizations in the world. The Headquarters Laboratory is located in Heidelberg (Germany) and the outstations are in Grenoble (France), Hamburg (Germany), Hinxton (UK), Barcelona (Spain) and Monterotondo (Italy).

The Prevedel group in Heidelberg is seeking a postdoc with an applied physics and/or engineering background to work on advanced imaging techniques for applications in biology and neuroscience. In particular, the candidate will work on the development of new optical instrumentation with a particular emphasis on optical wavefront shaping as well as photo-acoustic sensing. Additionally, the successful candidate will team-up with chemists as well as various biologists to apply the advances to current scientific questions.

This project is part of a recently funded Chan-Zuckerberg Initiative Deep-Tissue grant, and performed in close collaboration with the Deo group at EMBL, the Gigan/deAguiar  groups in Paris and the Stiel lab in Munich. The successful candidate is expected to take a strong lead on his/her project and start to develop independent ideas. Our multi-disciplinary lab provides an innovative, dynamic and constructive working environment for this purpose. More information about the Prevedel group can be found here: www.prevedel.embl.de

The successful candidate will work on a cutting-edge experimental research project in the structure-function analysis of chromatin in archaea. The goal of the project is to understand how eukaryotes evolved histone tails and tail-based regulatory mechanisms from an archaeal ancestor, and what role could the tails play in proto-eukaryotic chromatin. Practically, we will merge tail-less histones of a model archaeon with selected eukaryotic or Asgard histone tails in vitro and in vivo and investigate the structural and functional effects on a molecular level.

The project involves a combination of biochemistry, biophysics, and structural biology work. Structural part of the project will involve cryo-EM single particle analysis of nucleosomes, as well as FIB-milling of archaeal cells, and cryo electron tomography (cryo-ET) and subtomogram averaging of chromatin fibres.

Moreover, this project is a close collaboration with the laboratory of Naomichi Takemata (Kyoto University), and will involve regular communication and scientific exchange with the collaborators.

We invite applications for a Postdoctoral Fellow position in the field of Cancer Inequities as part of the Cancer Grand Challenges team SAMBAI (funded by CRUK and the NIH – Award reference 1 OT2 CA297567). We are looking for an intrinsically motivated, talented, and hypothesis-driven individual with experience in analysing high-throughput sequencing data sets and a passion for cancer inequities research.

This position represents a unique opportunity for individuals looking to gain in-depth knowledge and expertise in the study of the multifactorial basis of cancer inequities in the context of the Cancer Grand Challenge team SAMBAI, which involves world-leading investigators in this field and offers unparalleled opportunities for career development and growth. Working as part of the genomics analysis team of SAMBAI, you will interact with numerous researchers in the UK, US and Africa. More information about team SAMBAI can be found here: https://cancergrandchallenges.org/teams/sambai

You will be expected to lead the development of scalable and creative computational solutions for sequencing data analysis with a strong focus on developing methods applicable in limited-resource settings and compatible with real-world clinical samples. Specifically, your project will involve developing computational methods for analysing a unique collection of thousands of short and long-read whole-genome sequencing data sets from breast, pancreatic and prostate tumours. A major aim of this role is to integrate social determinants of health, exposomics, germline genetics, tumour genomics and immune profiling data sets to decode the relative contribution and interactions of these factors to cancer inequities in underserved populations of African descent. Thus, it is essential that you are dedicated to lead your own project while also being a team player and willing to engage with our national and international collaborators.