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Field
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-driven to take a deep dive into the unknown. You’re extremely capable, using creativity and ingenuity to rise to new challenges. You’ve got an excellent M.Sc. degree in cancer genetics, molecular biology
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: Building interpretable causal models to explain patterns (e.g., congestion dynamics), enabling transparency in high-stakes decision-making. We combine statistical data mining, deep learning, and domain
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generating a high-quality training dataset to support the development of the AI foundation model Contributing to the design and implementation of advanced deep learning architectures (e.g., Transformers, CNNs
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
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collaboration with Q.ANT GmbH in Stuttgart, a deep-tech company that develops photonic computing and photonic sensing products. The goal of this project is the development of highly integrated vapor cells with
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools