56 python "https:" "BCAM The Basque Center for Applied Mathematics" Fellowship positions at Nature Careers
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Computer Science, Biomedical Engineering, Statistics, Biomedical Sciences, Electrical Engineering, or a related quantitative field Strong programming skills (e.g., Python, R, or similar) Demonstrated research
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datasets using Python or R. Ability to work both independently and collaboratively, with strong organisation, attention to detail and communication skills. Clear motivation to apply interdisciplinary and
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proficiency in R and/or Python for statistical modeling and large-scale data analysis; experience developing reproducible computational workflows is preferred Experience with Linux-based environments and large
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compositionality with large language models. * Strong Python and ML ecosystem skills (e.g., PyTorch, scikit-learn, etc.). * Facility with C/C++/Rust or other systems-level programming languages * Skilled in writing
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reporting standards * Familiarity with deep learning technologies (e.g., CNNs, transformers) * Experience with Python and ML ecosystem skills (e.g., PyTorch, scikit-learn, etc.). * Skilled in writing and able
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learning, or a related field Strong background in deep learning and statistical analysis Proficiency in Python, R, and deep learning frameworks (e.g. PyTorch, TensorFlow) Strong written and verbal
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The Jiang Lab (https://labs.icahn.mssm.edu/jianglab/) at the Icahn School of Medicine at Mount Sinai, New York is seeking a highly motivated postdoctoral fellow with expertise in genome editing
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collaborators) and guide PhD students in their research. The ability to work in a team is essential. More information https://www.mdanderson.org/research/departments-labs-institutes/labs/van-loo-laboratory.html
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spatial transcriptomics to identify potential therapeutics for targeting GBM. For more details, please refer to articles: (Nature communications 2022, https://doi.org/10.1038/s41467-022-33943-0, Cell
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)] ▸ https://drive.google.com/file/d/1YcPi1Io07NbewRqsw6UJGmBjl73q1tvp/view [Bio2Q Website] ▸ https://bio2q.keio.ac.jp/ [Nillegoda Lab Website] ▸ https://www.nillegodalab.info/home