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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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design Git, and Github flow method of raising and merging pull requests Python JSON schema definition and use Mentoring junior designers/developers Fluent English Additional Asset: Experience writing
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transcriptomic/proteomic data analysis, or related analyses. Proficiency in Python and/or R, with experience in high-dimensional data visualization and integration. Experience with cell culture, tissue processing
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imaging modalities · Strong proficiency in Python for scientific computing, including experience with machine learning and image processing libraries such as PyTorch, TensorFlow, NumPy, SciPy, or OpenCV
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study. Demonstrated experience in NGS data analysis, programming (Python, R, Bash), molecular biology, microscopy, flow cytometry, statistical analysis, and biochemistry. Proper data management and
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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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or Python desired)
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difference in a collaborative and multidisciplinary environment will be preferred. Applicants should have experience of programming in Matlab and/or Python. Experience with grant writing and fellowship
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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