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Field
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221 of the Labor Code). We process the remaining personal data you provided us with in your application documents (including your PESEL number, image, or other additional data, if you chose to provide
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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work. Qualifications PhD in computer science, computational biology, engineering, or related fields. Experience developing deep-learning tools for image processing, automatic monitoring of agricultural
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Austrian Academy of Sciences, the Austrian Archaeological Institute OeAI | Austria | about 1 month ago
based in the research group “Historical Building Research” (Head G. Styhler-Aydın) of the Department of Historical Archaeology (see https://www.oeaw.ac.at/en/oeai/research/historical-archaeology
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experience: behavioral and/or molecular neuroscience; or bioinformatics; Skills: brain surgery and behavioral tests on rodents, or advanced image and data analysis; Experience in iDISCO brain clearing, light
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
role responsible for generating spatial transcriptomic and proteomic optical imaging data of human and non-human primate brains, utilizing the novel technology pipeline established by the Chung Lab
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. Postdoctoral researchers will have access to state-of-the-art imaging and cellular biology platforms, as well as a dynamic international research environment. Where to apply Website https://emploi.cnrs.fr
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. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ .
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. Experience in neurobiology of addiction, animal behavior (self-administration, stereotaxic surgery), and/or molecular techniques (PCR, western blot, immunohistochemistry). Experience in neuronal imaging is a
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transferable and interpretable models for tabular data, efficient learning paradigms for medical imaging, and causally grounded and identifiable representation learning. You will have great freedom to influence