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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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image/signal processing, particularly in computer vision.Strong programming skills and experience with at least one deep learning framework e.g. TensorFlow or PyTorchFamiliarity with machine learning and
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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venuesStrong programming skillsSolid mathematical foundation, including linear algebra, probability, statistics, and optimizationBroad and in-depth experience with machine learning algorithms and deep learning
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/ knowledge in the following areas: Solid background in motion planning and control of mobile robots Background in SLAM and SA models Background in Reinforcement and Deep Learning in robotics with a focus on
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terrestrial networks, non-terrestrial network entanglement distribution. Your profile PhD degree in wireless communications, signal processing, machine/deep learning or a closely related field in Electrical and
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with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g., ROS, Gazebo, Mujoco, IsaacSim
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep
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spectral imaging, lifetime data, or multi-channel image datasets. Solid background in chemometrics, machine learning, or deep learning, particularly for classification, clustering, or pattern recognition in