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- Max Planck Institute (MPI) for Psycholinguistics
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- Princess Máxima Center for Pediatric Oncology
- Princess Máxima Center for Pediatric Oncology; Utrecht
- The Netherlands Cancer Institute
- The Netherlands Cancer Institute; Amsterdam
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Field
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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object
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Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on
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. Exploring how these models can enhance human-computer interaction and improve our understanding of multimodal communication.To do so, you will have full access to motion-capture and virtual-reality labs, 3D
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. Exploring how these models can enhance human-computer interaction and improve our understanding of multimodal communication.To do so, you will have full access to motion-capture and virtual-reality labs, 3D
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single-cell and spatial transcriptomics, whole-genome sequencing, high-throughput screening, CRISPR-based functional screening, and patient-derived organoid models. You will work closely with in-house
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. Exploring how these models can enhance human-computer interaction and improve our understanding of multimodal communication.To do so, you will have full access to motion-capture and virtual-reality labs, 3D
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, high-throughput screening, CRISPR-based functional screening, and patient-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility
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settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on obtaining external funding and project
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, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor segmentation, enabling
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interpretation is subjective, heavily relying on clinician expertise. This project funded by the Hanarth fund combines ultrasound imaging with histopathology data to train advanced AI models for automatic tumor