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the Job related to staff position within a Research Infrastructure? No Offer Description Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations The simulation
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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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optical simulation skills (Preferably in Zemax) Strong programming skills (preferably in Python) Experience in deep learning algorithms is a plus Ability to work in a highly international team and
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cytometry panel for deep immunophenotyping of murine lungs. Baumann Z et al., Cell Rep Methods. 2024. 5 - Microenvironmental Regulation of Dormancy in Breast Cancer Metastasis: "An Ally that Changes
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annotation, and emerging machine-learning and generative methods for spectra or structure proposals. Evaluate and test emerging technologies (hardware and software) in close interaction with collaborators and
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of scientific results through publications and on conferences. Your profile PhD degree in materials science, physics or chemistry. Deep knowledge in X-ray diffraction and scattering methods. Additional knowledge
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of learning and memory, with a particular emphasis on cognitive disorders such as PTSD and Alzheimer's Disease. Specifically, we are looking for a person with a background in (neuro-) epigenetic research who is
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in workshops and training sessions to learn new techniques. A vibrant, international, and multidisciplinary environment that strives to be inclusive to people from diverse backgrounds. How to apply
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reasoning and interaction capabilities including RAG, in-context learning, chain-of-thought reasoning, and agentic workflows with external tools. Trustworthy AI evaluation and confidential deployment: develop