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Field
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden Institute
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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
. To gain deep insights into the fundamental physics of such flows, you will perform cutting-edge DNS on state-of-the-art high-performance computers. Leveraging conventional physical analyses and innovative
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implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning, signal processing
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PhD in Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
of two main parts: Improve methods for automatically aligning ontologies and linking data by leveraging the scalability, approximation, and multi-viewpoint capabilities of deep learning methods. Study
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strong interest in foundational research in the above-mentioned research areas strong programming skills, preferably in Python, including experience with deep learning frameworks (e.g., PyTorch, TensorFlow