353 deep-learning-phd-"https:" "Computer Vision Center" Postdoctoral positions in France
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at the crossroads of AI, machine learning, bioinformatics and genomics, and in developing new methods rather than just applying existing ones, we'd like to hear from you. Website for additional job details https
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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communication signals on our mental processes. What you will do Build novel deep learning architectures for auditory prediction (speech and/or music) prioritizing explainability and cognitive hierarchies Quantify
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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) enables computations to be performed directly on encrypted data without knowledge of the deciphering key, offering significant potential for privacy-preserving deep learning. However, conventional neural
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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photoactivation to treat deep-seated tumours without photodamage to healthy tissue. Targeting moiety for receptors overexpressed in PDAC cancer will be conjugated to the photosensitisers to specifically generate
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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evaluate innovative methods based on generative models and Vision-Language Models. Design, implement, and validate deep learning approaches for vision applications. Publish research results in leading