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the Master in Architecture under the supervision of Prof. Dr. Florian Hertweck.
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force fields (MLFFs) that combine state-of-the-art equivariant neural network architectures with robust, well-calibrated uncertainty estimates. These models will enable fully automated active learning in
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translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model–graph neural network architectures for gene perturbation prediction, including the design and
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inversion methods (LUT and hybrid approaches) Profound knowledge in machine learning and deep learning methods for remote sensing applications, including architectures such as CNNs, LSTMs, and Transformers
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knowledge in machine learning and deep learning methods for remote sensing applications, including architectures such as CNNs, LSTMs, and Transformers, and deep generative models (e.g., VAEs, normalizing
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). The team focuses on the development and design of reliable, safe, and secure software systems, carrying out both upstream activities such as requirements quality assurance and architecture analysis, as
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teams in TelecomAI Lab to integrate AI models and tools into advanced telecommunication system architectures Support lab activities related to designing and optimizing AI implementations across various
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of hybrid foundation model-graph neural network architectures for gene perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g