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Field
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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focuses on the analysis of neuronal networks in the Drosophila brain, and the Hummel team currently consists of postdocs, pre-docs, master students and administrative colleagues who share a common interest
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development. This entitlement, from the Concordat to Support the Career Development of Researchers , applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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to address urgent challenges in animal conservation and welfare. However, existing technologies have mostly been developed for use in controlled laboratory settings and are often unsuitable for field
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can personalize and automate home environments to improve comfort for residents, including temperature control, lighting, and air quality. Objective 3: Assess the role of digital twin technology in
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integration, and real-time optimisation, the project will ultimately help develop an adaptive system that helps pilots and controllers make smarter decisions mid-flight. The research will advance through three
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robotic automation to identify and optimise lead compounds. This approach will serve as a test case for a generalisable platform for rapid, structure-guided antiviral discovery. Approach and Methods
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engineering applications, and their tight integration with planning and control (e.g., task-and-motion planning, differentiable planning, or Reinforcement Learning (RL) with safety constraints). There will be