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Languages and Texts” doctoral program of the BerGSAS, https://www.berliner-antike-kolleg.org/en/bergsas/index.html We welcome applications from highly qualified graduates from the fields of: Ancient History
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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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transcriptomics and single-cell RNA sequencing on patient samples • Mining and analyzing public cancer databases (TCGA, GEO, etc.) and omics data • Inferring TLS formation and maturation stages from
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datasets (e.g. scRNA-seq) to derive prior knowledge on AML dormancy Develop and apply integrative data analysis pipelines (e.g. MOFA, Scriabin, LIANA+, COSMOS) for mining and interpreting multi-omic datasets
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-Checking, Argument Mining, Automated Planning, and Decision-Making. Training, domain adaptation, and evaluation of cutting-edge LLMs and Multi-Modal models in the cloud and on premise. Software Engineering
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Germany’s leading technical universities. German Text: Der Lehrstuhl für Effiziente Algorithmen unter der Leitung von Prof. Stephen Kobourov schreibt eine voll finanzierte Doktorandenstelle an der Technischen
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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
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associated with project C2 “Signaling and Interpreting Defectivity in Common Ground: Face-to-Face, Voice-Only, and Text-Only Communication” of CRC 1718. The aim of this interdisciplinary, cross-linguistic
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: Develop 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
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geriatric care in the Strukturwandelregion (former coal-mining region) of Saxony-Anhalt. The successful candidate will observe, follow, and engage various stakeholders (tech companies, care workers, local