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/ The underlying project is under supervision of Prof. Dr. Peter Zaspel and Prof. Dr. Michael Günther. The team of Prof. Peter Zaspel is located at Bergische Universität Wuppertal. The international team focuses
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academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
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mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and experience with the Linux operating system
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: economic sociology, historical sociology, (historical) political economy, historical social science, computational sociology - advanced skill in quantitative, statistical methods (R, stata, Python), ideally
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additive manufacturing of lightweight structures to enable novel development of materials and process design. The PhD position will be supervised by Prof. Noomane Ben Khalifa (Hereon/Leuphana University
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 19 days ago
collaboration with Dr. Mohsen Bagheri and Prof. Dr. Eberhard Bodenschatz. We are an international research institute conducting both experimental and theoretical fundamental scientific research and currently
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of materials and process design. The PhD position will be supervised by Prof. Noomane Ben Khalifa (Hereon/ Leuphana University Lüneburg) and supported by Dr.-Ing. Frederic Bock (Hereon). The objective is to
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, ideally with AI capabilities. Strong proficiency in technologies such as: TypeScript/React, Python/FastAPI, PostgreSQL/MongoDB Docker, Kubernetes, GitLab CI/CD, Terraform LLM integration (e.g., Hugging Face
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engineering or related courses with good to very good grades Programming experience, especially good to very good knowledge of Python Experience with machine learning, in particular with neural networks and
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prostate tumor samples. This position requires a strong background in both experimental proteomics and computational data science (R and Python), with an emphasis on LC-MS/MS workflows and long-term cohort