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Field
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trials and deep learning-based modeling of multimodal biomedical data. We are seeking two highly motivated scientists with expertise in Causal Inference / Statistics / Deep Learning / Bioinformatics
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The transition toward smart, energy-efficient buildings requires a deep understanding of how occupants actually use space and energy over time. Office buildings generate rich, continuous streams
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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close collaboration with a deep-tech startup. You will work in an internationally recognized research environment with access to state-of-the-art cleanroom facilities and collaborate with leading academic
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
contexts Strong programming skills and deep learning frameworks Familiarity with generative models (e.g., diffusion, GANs, autoencoders) or virtual staining concepts Excellent teamwork and communication
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about applying deep learning to decode the regulatory grammar of plant genomes and translating predictions into testable biological hypotheses, we invite you to join the Omics Data Analysis and
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this area, in particular, within the mathematical aspects of one or several of the following sub-areas: theory of deep learning, optimization in machine learning and data science, signal and image processing
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
candidate is expected to incorporate innovative approaches into their research that connect classical probabilistic models with modern deep learning architectures. Examples include Bayesian deep learning
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Biology: Development of deep learning, graph neural network, generative AI, and RNA foundation models to analyze large-scale omics, spatial transcriptomics, ribonomics, and imaging datasets. Application
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Learning. Experience with the deep learning ecosystem and high-performance computing infrastructures. Experience designing and conducting experiments with human participants is a strong plus. Experience in