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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 3 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
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the analysis of complex biomedical data using state-of-the-art AI and agentic system approaches, as well as the development of novel machine learning and deep learning algorithms. Your work will range from
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, a novel spatial discovery proteomics concept that integrates microscopic cell phenotyping with deep-learning based image analysis and global MS-based proteomics. This unique method was recently
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) and related to the major multi-national initiative “GOE-DEEP” supported by the International Continental Scientific Drilling Program (ICDP) and aiming to study the climate at the time of the first rise
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Python and common deep learning frameworks (PyTorch) • Excellent communication skills in English (German is a plus) • Independent, structured working style and experience in supervising students Preferred
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models and transformer-based architectures to construct high-dimensional design spaces. These models are integrated with Deep Reinforcement Learning (DRL) for fine-tuning or end-to-end learning, enabling
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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approaches beyond standard deep learning methods. Demonstrated experience in academic research (e. g. through publications, conference contributions or comparable scientific achievements). Very good
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research tasks, mentoring junior researchers, and coordinating multi-stage projects. Fluency in Python and modern deep learning frameworks (PyTorch/TensorFlow). Strong analytical, communication, and academic
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in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C