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Field
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As a PhD student, you will be embedded in the research group Computational Imaging and
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techniques, as well as cell culturing techniques Molecular biology techniques and digital image analysis Analysis of high-throughput datasets (RNA seq, proteomic data) Publications in international peer
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Deutsches Zentrum für Neurodegenerative Erkrankungen | Bonn, Nordrhein Westfalen | Germany | about 1 month ago
for an enthusiastic and ambitious early-career researcher with a strong interest in investigating multi-omic and multimodal brain imaging signatures of glymphatic function in the context of aging and cerebral small
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enhancing low-quality facial images. However, there is little scientific evidence on whether these tools improve human accuracy, how they affect machine recognition, and whether they introduce demographic
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data integration data quality data analytics programming languages (SQL, Python, Java) Complementary IT skills: machine learning image recognition sensor technologies robotic technologies
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projector in image reconstruction. Dual-energy imaging will be explored for quantitative determination of electron density (ρₑ) and effective atomic number (Zeff), for the inference of WAC-relevant indicators
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and NV-centres in diamond. The characterisation methodology will be developed, feeding into the creation of a prototype device. Job requirements and responsibilities: Characterisation of novel chip
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their mechanism of action using confocal microscopy, protein chemistry, high-content imaging and high-throughput screening methods combined with drug design and development. Compounds will be tested in standard
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in order to improve imaging techniques at large radio astronomy facilities. The project will be carried out in the Cosmology and Astroparticle Physics working group (Prof. Schwarz) at the Faculty
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. MAIAMI aims to establish a paradigm shift in materials science by embedding mechanistic AI agents into physical experimentation cycles—enabling rapid, transparent, and reproducible design of advanced