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Field
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clinical use-cases for benchmarking existing state-of-the-art (SOTA) Federated Learning algorithms. This includes running a few pre-processing pipelines. Develop SOTA FL algorithms that tackle data
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matter physics, biomedical/material engineering or a related discipline. You have a strong background in data analysis and image processing. You enjoy working in interdisciplinary and international teams
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Enthusiasm for an exciting new computing paradigm involving the development of innovative solutions Openness to communicate, cooperate and exchange ideas within a joint endeavor of multiple vibrant research
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, or in a related discipline Profound data processing and image analysis skills Good knowledge in programming, ideally in Python Ability to work self-reliant and to collaborate in a team of scientists
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the research areas of Infection Medicine and Microbiology, Immunology, Oncology, Neurosciences, Pharmacology/Cardiology/Vascular Medicine, Imaging Technology and Biomedical Engineering, amongst others. A large
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(SOTA) Federated Learning algorithms. This includes running a few pre-processing pipelines. Develop SOTA algorithms in Robustness against data and model poisoning attacks. Develop SOTA algorithms in
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of acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text
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simulators (on various abstraction levels using, e.g., Computational Fluid Dynamics) which enables us to verify designs of microfluidic devices even before the first prototype is fabricated. Fabrication: We
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of microfluidic devices even before the first prototype is fabricated. In this field, we are involved in a consortial project with stakeholders from academia and industry to establish those tools for practical
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team Other welcomed qualifications knowledge of signal processing algorithms for images, videos or audio is welcomed good graphic design skills with tools such powerpoint and photoshop is welcomed