95 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" PhD research jobs
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(25260475) Responsibilities: The Department is recruiting a scholar at the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related
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Professor (25260446) Responsibilities: The Department is recruiting one scholar at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics
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Networks. Knowledge of and experience in Python, TensorFlow, Keras, or other Machine Learning toolboxes, is essential. Knowledge of and experience in Large Language Models is highly relevant. The successful
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or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers
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the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related areas. The appointee is expected to conduct high-impact research
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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of visualisation, machine learning, and human-computer interaction under the joint supervision of both institutions. The position is shared by TU Wien and USTP and offers the opportunity to conduct research at both
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-stage researchers in advanced data analytics, causal inference and machine learning related to health policy topics. Specifically, it is training them to evaluate real-world policy impacts. Focusing
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities