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-page summary explaining your past research, your interests and how joining the Acharya Lab fits within your overall career objectives, and the contact information for 3 references. You will be contacted
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of methodologists, clinical academics, and PhD students at the University of Warwick with a strong track record in world-leading cardiac arrest research. You are required to have an upper-second class honours degree
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doctoral student who has Competence and skills: -Strong foundation in computer vision (e.g., object detection, image segmentation) and NLP (e.g., text reasoning, language models). -Knowledge of generative AI
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reconstruction, and the need to evaluate generated and transmitted data in terms of their relevance and utility for achieving specific objectives. To address these challenges, the project will develop theoretical
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the focus of this PhD project. The objectives of this work are to optimize an experimental setup for simultaneous tension/compression testing and localized corrosion measurements, including monitoring
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niches. Building on our experience in cell-fate tracking tracking and studying various stages of the metastatic cascade, we set out to follow tumour cell-niche interactions to reveal how distant sites
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portability - in cases provided by law, d) the right to object to data processing, e) the right to lodge a complaint to the supervisory authority - the President of the Office for Personal Data Protection
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of thousands of software engineers. Across several tracks, we will develop tools that rethink the future of software engineering. Our work will be informed by sound theories and supported by empirical data. In
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: The objective of the work consists in the production of active films incorporated with protein hydrolysates obtained from fish waste using super/subcritical fluids, their complete characterization
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 10 days ago
imaging datasets across modalities (X-ray, ultrasound, MRI). Scalable ML workflows: GPU-based training, experiment tracking, reproducible pipelines, model validation and deployment. Research excellence