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in radiotherapy with the goal of enabling fully adaptive radiotherapy. The work is based on deep learning, where models are trained on generated or clinical data. The project is carried out in
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for early-stage cancer using statistics and/or machine learning (including deep learning where appropriate). You will join a vibrant and growing research group of 12 scientists (six postdoctoral researchers
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the appointment start date; demonstrate strong expertise in computational biology or data-driven modeling, with experience in one or more of the following areas: machine learning or deep learning, structural
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks
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bioinformatics, deep learning and/or biomedical image and clinical data analysis (e.g. Linux, R programming) is essential. The appointees will need to perform data analysis of single cell RNA-sequencing
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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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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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reconstruction and processing. Candidates are expected to have a strong background in medical imaging, experience in imaging system evaluation, deep learning and clinical investigation. Brief Description of Duties
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image