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Description The DPF Instrumentation Award and DPF Instrumentation Early Career Award are bestowed annually to honor exceptional contributions to instrumentation advancing the field of particle physics through the invention, refinement, or application of instrumentation and detectors. The awards...
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The Faculty of Science and Engineering (FSE) provides teaching and research across a wide range of disciplines, from physics and biology to artificial intelligence, mechanical engineering, and pharmacy. In close collaboration with partners from industry, healthcare, and society, we contribute to...
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research environment within MIPCART and the systemic radionuclide therapy activities at the Division of Oncology, Department of Clinical Sciences, Lund University. This interdisciplinary composition enables
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areas of nanoscience and nanotechnology. Job Title: PhD STUDENT (INTRANASAL TREATMENT AND IMAGING OF NEUROLOGICAL DISORDERS USING NANOPARTICLES) Research area or group: Nanostructured Functional Materials
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organismal levels • Gene expression analysis (histology, scRNAseq data minining) • Phenotyping using behavioural setups • Acquisition and segmentation of image datasets • Bioinformatic data analysis (genome
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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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About the project: SEGregation of residual eleMENTs at austenite grain boundaries in recycled steels (SEGMENT) Supervisor: Dr Michael Auinger, University of Warwick Steel recycling is a key strategy
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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
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their website at https://www.naritalab.com/ Our laboratory investigates the biology of preneoplastic cell states, with particular interest in how changes in cell functional identity and plasticity are controlled
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show