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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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terrestrial system models, for example using data analysis methods, such as data assimilation, physical- or process-based machine learning, or deep learning algorithms Analysis of the effects of human
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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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. It would be an extra, if you had experience or interest in learning to use AI-Assistants and LLMs for research purposes, e.g., Deep Research, As well as having experience or interest in learning to use
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on three areas: strategy&technology (in particular deep tech such as AI, robotics, biotech), strategy&global contexts, and the theory of the firm (see www.msl.mgt.tum.de/simanagement ) We do not only
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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Computer Science, Artificial Intelligence, Materials Science, or a related field Strong programming skills in Python, ideally with experience in image processing and deep learning using PyTorch or similar frameworks
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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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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early