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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
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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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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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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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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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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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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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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