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for detecting visual stress markers using underwater cameras, and other imaging modalities in industrial environments (RAS farm) Develop and validate pipelines for fish biometrics, posture and movement analysis
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Solution Centre SAS-C. Key Responsibilities: Research and develop AI-assisted methodologies and tools for anomaly and stress pattern detection in aquaculture systems. Focus on multi-modal sensor data
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the lab Develop AI-powered pipelines for analysis of microendoscopic calcium imaging, confocal fluorescence microscopy, and behavioral videos Lead cross-modality image registration applications Launch new
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of hands-on experience in neuroimaging research, particularly in MRI/EEG/fNIRS data acquisition, postprocessing and analysis Proven expertise in MRI postprocessing pipelines, experience with other modalities
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analysis and inversion of gravity gradient data from quantum technology sensors Develop data interfaces to enable integration with multi-modal sensing platforms Apply advanced data inference techniques
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% - Ultrasound Data Collection and Data Analysis 30%-Conference Abstract and Manuscript Preparation Required Qualifications* PhD in Mechanical Engineering, Electrical Engineering, Computer Science, or related
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of clinical decision support systems and multimodal analytic platforms to translate advanced data science solutions into critical care. The main focus of this position is on the analysis of the signals
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approaches to assessing the human pancreas, including live pancreas slice methodologies that may include cellular imaging or labelling approaches Leveraging complex multi-modal phenotyping data to develop new
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and motivate others to do the same Multi modal live imaging and mammalian cell biology Hands-on experience in imaging Strong understanding of image analysis Desirable Experience with relevant software
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to work on drug target discovery and therapeutics in genomically unstable adult and pediatric cancers. The project is tackling an unmet need by identifying modality combinations of antibodies/ADCs and small