PhD position in Self-Supervised Learning for Anomaly Detection in Medical Neuroimaging
il y a 5 jours
Type de structure : We offer a stimulating research environment gathering experts in Image processing, Neurosciences & Neuroimaging, Advanced Statistical and Machine Learning methods from CREATIS, Grenoble Institute of Neurosciences (GIN) and INRIA. The PhD position is granted by the “Défi IA” program sponsored by la Région Auvergne Rhône-Alpes.The position is available in the framework of DAISIES project.Contexte et mission : Scientific contextThe vast majority of deep learning architectures for medical image analysis are based on supervised models requiring thecollection of large datasets of annotated examples. Building such annotated datasets, which requires skilled medical experts,is time consuming and hardly achievable, especially for some specific tasks, including the detection of small and subtle lesionsthat are sometimes impossible to visually detect and thus manually outline. This critical aspect significantly impairsperformances of supervised models and hampers their deployment in clinical neuroimaging applications, especially for brainpathologies that require the detection of small size lesions (e.g. multiple sclerosis, microbleeds) or subtle structural ormorphological changes (e.g. Parkinson disease).OBJECTIVE AND RESEARCH PROGRAM :To solve this challenging issue, the objective of this thesis is to develop and evaluate deep self-supervised detection andsegmentation approaches whose training does not require any fine semantic annotations of the anomalies localization. We willexplore different categories of self-supervised methods, including: novel unsupervised auto-encoder based anomaly detectionmodels leveraging on the recent developments in visual transformers blocks (ViT) or vector quantized variational autoencoders(VQ-VAE), scalability of Gaussian mixture models as well as weakly supervised models based on scarce annotations.Key words: Machine learning, Deep Learning; Multidimensional data, Segmentation, Neuroimaging, Self-supervised learning,Anomaly detection, Unsupervised representation learningStarting date: Autumn 2022How to apply: Send an email directly to three supervisors with your CV and persons to contact. Interviews of the selectedapplicants will be done on an ongoing basis. Applications will be accepted up to the 30st of June.Lieu : Location: Grenoble Neurosciences Institute: https://neurosciences.univ-grenoble-alpes.fr & CREATIS - Villeurbanne: https://www.creatis.insa-lyon.fr/. Time sharing in the two laboratories will be discussed with the selected candidates.Contact : The PhD candidate will be co-supervised by: - GIN - team «Functional neuroimaging and brain perfusion»: Michel Dojat (michel.dojat@inserm.fr), - CREATIS - team Myriad : Carole Lartizien (carole.lartizien@creatis.insa-lyon.fr) - INRIA - team Statify: Florence Forbes (Florence.forbes@inria.fr) #J-18808-Ljbffr
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Postdoctoral researcher in cognitive neuroscience
il y a 7 jours
Grenoble, France Karlstad University Temps pleinPostdoctoral researcher in cognitive neuroscience Type of recruitment: Fixed-term contract position Job level: A - Researcher Contract duration: From 1 February 2026 to 31 January 2027 Presentation of the organisation: The Grenoble Institute of Neurosciences (GIN, Inserm U1216, Université Grenoble Alpes) is a multidisciplinary research centre dedicated to...
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Grenoble, France Karlstad University Temps pleinPostdoctoral researcher in molecular biology and neurobiology Type of recruitment: Fixed-term contract position Job level: A - Researcher Contract duration: 1 February 2026 to 31 January 2029 Presentation of the organisationGIN brings together teams in Grenoble specialising in the study of physiological and pathological processes of the nervous system and...
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PhD Candidate: Time-Resolved Mechanisms in Dioxygenases
il y a 7 jours
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Stage: In-painting Using Generative Ai for The
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Grenoble, France ESRF - The European Synchrotron Temps pleinScientist for Machine Learning Synchrotron Applications Join to apply for the Scientist for Machine Learning Synchrotron Applications role at ESRF – The European Synchrotron 1 day ago – Be among the first 25 applicants. Context &Job Description The Extremely Brilliant Source upgrade of the ESRF has led to a significant increase in the acquisition speed...
