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Field
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planning. b) Have knowledge of statistical computing, data science, data modelling, or data visualisation languages and software (e.g., PowerBI, Python, R, SAS, SPSS, SQL, Tableau, etc.) c) Have experience
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. Computational modelling approaches are frequently used alongside experimental research programmes and much of our research crosses traditional boundaries, including the relationship of biodiversity to the health
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electric vehicle (EV) charging infrastructure. The selected candidate will contribute to the modeling, simulation, and prototyping of innovative hardware solutions, including harmonic mitigation and thermal
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clinical studies, including challenge human infection models. Collaborate on Clinical Trials: Work closely with clinical collaborators to implement statistical methods for ongoing trials at the Doherty
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the partnership, the Associate will develop the ability to: (i) model and predict fluid flow and system performance for large-scale systems; (ii) assess how different types of fouling may affect the membranes and
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research findings at scientific conferences. Mentor and supervise graduate students and research assistants. Stay current with the latest developments in cancer research and contribute to new project ideas
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working with NLP in general and LLMs in particular. They will also help to further develop machine learning models to predict clinical outcomes. Familiarity with current methods in this area is essential
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observation and experimentation), data modeling and analysis, and manuscript preparation for current laboratory projects (approximately 50-60%). External grant submissions for current and new laboratory
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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and
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performance, vessel resistance and propulsion using CFD Optimize designs for properller dynamic environmental conditions (e.g., waves, tides, currents). Finite Element and Structural Modeling: Develop and