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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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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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. 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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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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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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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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to research facilities in Germany and abroad Scientific writing skills evidenced in publications (for Postdocs) A deep sense of scientific curiosity and the aspiration for achieving knowledge in solid state
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, Genome editing tools, Regulatory mechanisms, Synthetic genomics, Genotype-to-phenotype & genomic-environment interactions, Metabolism, Single cell and spatial omics development Deep learning-enabled
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to molecular mechanism. New experimental and computational methods, including data and deep-learning driven approaches to study complex biological processes in the context of cells, organisms, communities and