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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | about 1 month ago
Prof. Dr. Eberhard Bodenschatz, who directs the Laboratory for Fluid Physics, Pattern Formation and Biocomplexity (LFPB). We are an international research institute conducting both experimental and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 7 hours ago
line of research designed to establish a paradigm shift in our biomechanical understanding and clinical management of age-related mobility impairment toward feasible and cost-effective devices to modify
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: Exempt Work Schedule: Monday – Friday, 8 a.m. – 5 p.m. Summary The applicant must have a Ph.D. in Physics, Optics, Bioengineering, or related field. Extensive expertise in optical imaging, light
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vision, controls, cyber-physical systems and their security, hardware security, and machine learning and their security. The work will include algorithm design, prototype implementation (e.g., in Matlab
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mechanisms through reaction kinetics analysis and other physical organic and inorganic techniques Perform detailed in situ / operando studies of catalysts using X-ray Absorption and Emission Spectroscopies
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within the Clusters of Excellence ‘Machine Learning for Science’ and ‘Image-Guided and Functionally Instructed Tumor Therapies (iFIT)’. Requirements PhD in Bioinformatics, Computational Biology, or a
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laboratory is seeking a motivated and highly skilled post-doctoral researcher to join our dynamic laboratory dedicated to studying HIV-1, with a particular focus on development of novel imaging approaches
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activation deconvoluted with single-molecule precision . Single-molecule analysis of ligand efficacy in β2AR-G-protein activation. Single-molecule FRET imaging of GPCR dimers in living cells Quantifying
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candidate will lead the design and modelling of thermally regulated DNA motifs and contribute to the development of an automated and renewable DNA computing prototype. The role encompasses both wet-lab and in
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence