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pathways driving leukemic transformation during haematopoietic development. The successful candidate will use in vitro differentiation systems (ESC/iPSC) to model normal and malignant haematopoiesis
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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. The GFZ contribution to the project focuses on providing global data-based reconstructions of past reversals and excursions. The postdoctoral researcher will work together with two PhD students on measuring
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biological systems, also in collaboration with other researchers and companies. Your profile Applicants should hold a PhD in Computer Science, Computer Engineering, Artificial Intelligence, Physics
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understanding and knowledge of climate dynamics. The candidate should have experience of statistical (multivariate) concepts and should be open to apply new and upcoming AI methods to analyze the climate
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that regulate B cell activation, antibody production and their genetic variability. We use a wide range of immunological and genomic assays to study B cell biology, antibody diversity and variation in the human
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, Computational Biology, Applied Mathematics or related field Interest in interdisciplinary research Applied experience with machine/deep learning methods Ability to handle multiple projects in a dynamic
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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, Machine Learning, Physics, Mathematics, Engineering, or equivalent A solid publication record with research publication(s) in peer-reviewed international journals Good programming skills (ideally in Python
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scaling and generalization behavior Roll out the model to the global user community Requirements PhD or MSc in computer science, physics, mathematics or a related discipline Experience with large-scale HPC