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. ● Demonstrated expertise in causal inference, with interest in methods development. ● Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning
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AWI - Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research | Bremerhaven, Bremen | Germany | about 2 months ago
of convection in the vertical exchange of climate-relevant tracers such as carbon and heat between the surface and deep ocean in the seasonally sea-ice covered Southern Ocean. Your Tasks You will contribute
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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raises deep and largely unsolved challenges. As a postdoctoral researcher, you will tackle exactly this question: how to generate code with AI and formally verify that it does what it should. Your work
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leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation
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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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communications, orthogonal time frequency space modulation, radar/sonar signal processing, and machine and deep learning. WORK PERFORMING: Individual will perform research generally in the area of statistical
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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-to-function deep learning models (e.g. Borzoi-, Enformer-, or AlphaGenome-style) to predict single-cell chromatin accessibility, histone modifications, higher-order chromatin organization, and gene expression