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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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for the following full-time postdoc position to begin as soon as possible. Postdoctoral position in organoid engineering and brain disease modeling (f/m/d) The position is funded by the Carl-Zeiss-Stiftung (CZS
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limited understanding of how tumors systemically impair immune responses. Meanwhile, traditional animal models often fail to capture the complexity of human immune-cancer interactions. This position
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models for inflammatory bowel diseases using primary cells from biopsy samples. Innovative development of complex co-culture systems combining patient-derived organoids with autologous immune cells
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Your Job: Develop techniques to simulate, control, and optimize the time-dependent dynamics for increasing system complexities Implement and optimize small quantum circuits on super- and semi
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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a resilience performance modelling framework at the regional and European level. The place of employment is Hamburg. GERICS develops science-based prototype products and services in support to
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models and neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio
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performing simulations with computational models of the Earth system on different levels of complexity scientific publication records appropriate for the experience level experience in scientific software