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(multiomics), CRISPR genome editing, deep learning, network modeling, confocal and two-photon live imaging. Please visit the Özel Lab Website for more information. Ideal candidates will be highly motivated and
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supported by an external team of deep-learning experts. You will also become an integral part of the Multiscale Cloud Physics Group currently being established by Dr Franziska Glassmeier at the Max Planck
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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from one round to the next, and eventually the library collapses to a few selected functional aptamers. The evolution can be tracked in detail by deep sequencing of the successive rounds. The goal
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deep learning approaches on a wide variety of clinical data modalities, such as structured data, imaging and testing data, and free-text clinical notes. The researcher will have the opportunity to work
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infrastructure. These efforts will directly enable innovative data analytical approaches, including federated and deep learning, with a focus on real-world data for rare cancers. This research will directly
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, e.g. experience fitting Reinforcement Learning models or applying Agent Based Modelling to human behavioural data. You should have a deep understanding of the strengths and limitations
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profiles, to identify treatment response patterns, subtypes, and critical intervention windows that reduce Alzheimer's risk Disease (AD) risk. This position will involve applying machine learning, deep
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing