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Field
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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Beginning Winter semester Application deadline All students – online application: 1 March for the following winter semester https://www.lmu.de/psy/de/studium/doctoral-training-program-in-the-learning-sciences
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qualities include: earlier research experience, e.g., as part of Masters’ studies, and familiarity with machine learning, formal methods or network protocols are considered as merits. Your workplace
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have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and spoken proficiency in (scientific) English is
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding
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to learn more about the project, and perhaps our group? Feel free to browse our webpages: About our department: QCE department . About our group: Computer Engineering Lab . Job requirements For this position
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Engineering, Science and Systems (DESS) research group focuses on data-intensive systems, spatio-temporal data management, data analytics, and applications of machine learning, with applications in digital energy and
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Developing new machine learning