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Field
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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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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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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following would be advantageous: Programming in Python or a comparable language. Using machine-learning frameworks such as PyTorch or TensorFlow. Working with image, video, time-series or sensor data
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systems and control, applied mathematics, engineering, or a related field A strong background or interest in systems and control, applied mathematics, machine learning, and affinity with biological systems
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, medical imaging, real-time sensing, and intelligent control into a unified system, this technology establishes a new paradigm for precision neurotherapeutics and opens new opportunities for treating
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Neuroscience. This position is one of four within a coordinated cluster hire - alongside colleagues in brain circuit imaging, machine learning and artificial intelligence, and research software engineering
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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with training neural networks to develop the next generation of optical microscopes. You will have the opportunity gain skills in optical instrumentation and imaging, AI and machine learning, and in
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning