47 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" PhD positions at Forschungszentrum Jülich
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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combined with soil sensors systems and UAVs with multispectral cameras. Your tasks in detail: Development and application of high resolution time-lapse GPR and EMI imaging methods at multiple scales
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on the following tasks with either with a stronger model-development or application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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research together with cutting-edge materials science and physics. Depending on your background you will work collaboratively on the following tasks with either with a stronger model-development or
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strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain
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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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, your work will contribute to establishing a fundamental understanding of the mechanical properties and microstructure of newly developed advanced ceramic materials for solid oxide electrolyzer cells
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exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain scientists on, e.g.: Developing self-supervised learning frameworks to extract