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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas
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cross encoder architectures and retrieval augmented generation. Strong programming skills in Python and deep learning frameworks, familiarity with MLOps practices, data governance for sensitive health
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desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong communication and collaboration skills; ability to work independently and as part of a team. Willingness to respect
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, or probabilistic modeling, and be proficient in Python and modern machine-learning frameworks (ideally PyTorch). Experience with single-cell transcriptomics, epigenomics, proteomics, spatial omics, or multimodal
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, or behavioral data) and be proficient in Python and modern deep-learning frameworks (ideally PyTorch). Experience in computer vision, multimodal data fusion, self-supervised or generative modeling is highly
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, computational cancer biology/genetics or a related field, obtained within the last 5 years by the time of the appointment start date or an M.D. within 10 years as well as Proficiency in Python, R, and ML
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., Seurat, Scanpy, DESeq2). Experience with spatial transcriptomics and multi-modal data integration is highly desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong
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in programming with languages commonly used in AI development (e.g., Python, R, Java, C++, JSON). Experience in designing, developing, and evaluating AI/Machine Learning models. Excellent analytical
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/validate algorithms for medical images and perform data analysis using state-of-the-art software programming tools such as Python. They are also expected to write publications, liaise with collaborators