77 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"ISCTE-IUL" positions in France
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-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1- Context and Objective: Monitoring tumor response using clinical imaging, such as CT or FDG-PET, has become a
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strong background in optimization and machine learning. Good coding skills in Python, PyTorch are welcomed. Application Applications should contain a CV, a motivation letter, the grade records of the last
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, biology, computer science or related disciplines Strong computational skills, including machine learning, e.g. demonstrable project in a relevant field A strong first-author publication record in a relevant
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of the scientific publications • Motivation letter • Letter of recommendation of the thesis supervisor Description of the topic: Change-point detection (CPD) is a fundamental problem in statistics and machine
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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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. The objective of this postdoctoral project is to develop a unified, AI-compatible framework for non-neural behavior based on dynamical systems and learning. Behaviors will be modeled as low-dimensional dynamical
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and output databases; deployment of methods and tools for data acquisition and visualization; * AI/Machine Learning/Deep learning techniques for model input and output data analysis and production, in