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project BEPREP (f/m/d) Your tasks: Apply and further develop machine learning methods for the analysis of health and climate data Conduct spatio-temporal analyses of patient and climate datasets to identify
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for Astronomy in Germany. The StarForML group focuses on developing robust machine learning tools for the evaluation of star formation observations. We aim to gain new insights into how star formation progresses
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infrastructure that enables secure, large-scale data integration across Europe. The postdoc will focus on two pillars: Multi-omics data integration: developing robust pipelines and frameworks for preprocessing
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computing Develop Infrastructure as Code (IaC) solutions using tools like Terraform or AWS CloudFormation Implement containerized compute environments using Kubernetes (EKS) or Docker-based orchestration
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data sharing and computing Further develop and maintain Infrastructure as Code (IaC) solutions using tools like Terraform or AWS CloudFormation Implement and maintain containerized compute environments
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, and biomedicine to develop reproducible, micrometer-scale, controllable, and cost-efficient 3D stem cell-derived disease models for applications in neuroscience, cardiology, and gastroenterology
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, individual training and development opportunities Working with the latest techniques / technical equipment Flexible working hours within the framework of flexitime Possibility of doctorate Possibility
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the Metabolomics Core Technology Platform (MCTP). We seek an enthusiastic scientist who will operate, develop, and continuously advance spatial metabolomics services, facilitating cutting-edge research across
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culture Develope new methods like gonadal organoids or work with primary patient’s cells Specific data entry, documentation, and archiving work Your profile Successfully completed a Master’s degree in Cell
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sensor data, with applications in disease modeling and the development of material science-based innovations. These efforts aim to optimize system performance and uncover novel biological insights in close