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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | 3 months ago
), is offering a fully funded PhD position in the area of statistical learning, machine learning, and survival analysis applied to large-scale proteomics and multi-omics cohort data. The PhD project
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on the research strengths in bioengineering, data analytics, artificial intelligence, and machine learning. More information on our research strengths can be found at https://www.uta.edu/academics/schools-colleges
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, through open data, open code, open educational resources, and practices that support replication. Desirable: F1 Experience of leading and teaching large postgraduate taught courses. F2 Experience
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PhD student will expect to develop some experience in developing power systems models using a range of computer languages and tools (e.g. Python, MATLAB, OPNET, etc), ideally for applications involving
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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) and Artificial Intelligence (AI). The Department envisions to cultivate a comprehensive curriculum that encompasses key research pillars such as Big Data Analytics and Management, Machine Learning and
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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of clinical and health informatics, systems interventions, community participatory research, human-computer interaction, usability, mobile technology, bioinformatics and biomedical engineering. Indiana is home
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computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The subject area concerns