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Field
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decision-making, computational efficiency, generalization under changing market conditions, and safe constraint handling. The PhD candidate will develop and validate decision-support methods based on deep
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. – knowledge of computer vision; knowledge of deep learning architectures; – Knowledge of C++, Python, Matlab; – Analog/digital circuits IC design capability; – Testing of electronic devices and systems; FPGA
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resources efficiently. In this PhD project, you will develop mathematical theory and computational methods for the analysis and design of chaotic sampling mechanisms in networked control systems. You will
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such as mechanistic, chemometric, deep learning, and physics-aware models. Improve robustness and reliability of the developed methods for deploying AI models in real environments utilizing augmentation
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project topics can be found at https://indibrain.eu/recruitment . OPEN POSITION: Project 5: Modeling Individual Brain Differences using Deep Learning & Vision Neuroscience: This project sits
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
bioinformatics or molecular biology position. We're looking for someone genuinely excited by the intersection of structural/molecular biology, physics-based simulation, and machine learning/deep learning – a
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of cryptographic implementations and hardware-security countermeasures. Experience with hardware reverse engineering, debugging interfaces, or firmware analysis. Experience with machine learning, deep learning
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in one or more of the following research areas is desirable: geometric numerical integration, structure preserving deep learning, stochastic differential equations, generative AI, numerical
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research. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a background in Natural
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computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP