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challenges. We are deeply committed to recruiting and retaining a talented workforce that embraces our core values of Respect, Excellence, Leadership, Loyalty, Integrity, and Service, by offering competitive
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.•Cell-based virus assays [multi-color flow cytometry, primary cell culture (is an advantage)].•Experience in animal experimentation (FELASA B/C or equivalent) and in the development of transgenic and
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interdisciplinary teams. Preferred Knowledge, Skills, and Abilities: Experience scaling machine learning training and/or inference on multi-node HPC systems. History of implementing, adapting, or optimizing machine
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 2 months ago
foundations of augmentations for digital pathology Potentially developing multi-scale AI models for cancer patient stratification combining LLMs, molecular measurements, and multi-scale imaging. Your profile
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About us The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Interdisciplinary Centre for Security, Reliability
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component and will employ significant multi-omic datasets available from our current IBD projects and requires the postholder to work closely in a multidisciplinary team of immunologists, clinical researchers
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laboratories, and other health care organizations. Our operations are based at the University of Washington Medical Center, Harborview Medical Center, South Lake Union, Northwest Hospital and the Seattle Cancer
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About Us The School of Biomedical Engineering & Imaging Sciences has attracted multi-million pound research funding and collaborates with other universities and Faculties within KCL, the NHS
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include Luxembourg and its neighbour regions. Using an innovative multi-method design, the project integrates: Daily diary and ecological momentary assessment (EMA) approaches In-depth qualitative and
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methods Supervising a small team of PhD and MSc students Developing new foundations of augmentations for digital pathology Potentially developing multi-scale AI models for cancer patient stratification