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Candidate (DC) to work on Work Package 3. As DC11, your research objectives are to: Harmonise genomic workflows and integrate AI for variant detection and outbreak forecasting Build secure, interoperable and
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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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Current University of Arkansas System employees, including student employees and graduate assistants, need to log in to Workday via MyApps.Microsoft.com , then access Find Jobs from the Workday
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to the activity. C2 Developing track record of research outputs compatible with enhancing the School’s research profile in accordance with School objectives. C3 Proven commitment to supporting
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chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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objective is to develop methods that move beyond correlation-based prediction toward causal reasoning, intervention-aware modelling, and interpretable AI systems. This transition from correlation to causation
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generation towards systems capable of planning, pursuing objectives, and executing long-horizon tasks. A central focus will be agentic safety, adversarial robustness, and AI control. This includes agent
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to scientific audiences as well as to external partners, stakeholders, and other end users of applied ecosystem science Expertise building Bayesian networks for ecological application Expertise working with
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, deepfake detection, and the question of how we can identify the origin of AI-generated content? We are looking for a Doctoral Researcher to join the funded project COWAMA: Content-based watermarking for AI