9 image-processing-and-machine-learning-"RMIT-University" Postdoctoral positions at Institut Pasteur in France
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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neurodegenerative processes. Investigate neuronal activity and neural stem cell fate in both acute and persistent infection Construct a hamster brain atlas under infection with temporal, spa.al and single cell
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A postdoctoral position is available to study dynamic processes in early development of insects and mammals. Successful candidates will join a collaborative and interdisciplinary venture in
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. Responsibilities will include: Developing expertise in audiological test batteries Data wrangling, cleaning, and feature engineering Applying and implementing statistical or machine learning methods, depending
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Keywords Optogenetics, 2-photon activation, large scale electrophysiological recordings, mouse model Lab description Processing of auditory information in the brain is complex because information
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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, biosynthesis and immune response. Key Responsibilities: • Conduct high-quality research on cell wall analysis of Candida species. • Using confocal microscopy imaging, flow cytometry and biochemistry and genomic
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benjamin.devauchelle@pasteur.fr in Cc. The candidate should have a PhD in Human Genetics and the following skills: Strong interest in the field of genetics, neurobiology and psychiatry. Computer skills: strong level in
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Postdoctoral position: Developing a human lymphoid organ-on-chip to evaluate candidate mRNA vaccines
immunology, with expertise in T cell/B cell interactions and/or vaccinology an expertise in advanced cytometry a strong motivation to learn organ-on-chip technologies a track record of publications in relevant