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- Knowledge in programming in Python or R - Familiarity with machine learning or deep learning methods is a plus - Interest in plant genomics, evolutionary biology, or comparative genomics - Proficient in
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(e.g. computer vision, deep learning, AI) and green life sciences (e.g., remote sensing, crop modelling, and food security), within the European funded project AgriscienceFM (Horizon programme), which
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previously acquired knowledge. We will study collaborative learning scenarios, where multiple devices or sensors jointly process and learn from data streams. Such settings introduce additional challenges
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London) has multiple, fully-funded PhD studentships available to accelerate its interdisciplinary research in the humanities, social sciences and digital sciences. Each scholarship is fully-funded
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integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases Deconvolution and TME characterization
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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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, spanning data preparation, acquisition, ingestion, integration, model development, training, and evaluation across multiple modalities. This position engages collaboratively with faculty, PhD students, and
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 20 days ago
mathematical background. - Familiarity with deep learning frameworks such as PyTorch. - Commitment, team working and a critical mind. - Fluent verbal and written communication skills in English
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, machine learning, deep learning, high powered computing (requiring Python etc) or a combination of data science and qualitative methods (e.g., interviews and focus groups). Project themes include (but
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). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with Reinforcement Learning, Deep Learning and/or Explainable AI, demonstrated for example through