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cancer models Experience with single-cell/single-nucleus genomics and spatial transcriptomics Computational analysis skills in R and/or Python A record of first-author publication To apply: Please send a
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language (C++, Python, Rust, …) One high-quality first-author paper (journal or top-tier conference) You are expected to be somewhat accustomed to teaching, and to demonstrate good potential within research
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bioinformatics, particularly single‑cell RNA‑seq, multi‑omics, and spatial transcriptomics analysis. Specific Requirements Proficiency in Python and R; familiarity with common bioinformatics tools and pipelines
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or equivalent Skills/Qualifications - Conocimientos demostrables de Python, Matlab, SageMath y MySQL. Nivel avanzado Specific Requirements - Máster en matemáticas y/o ingeniería computacional - Doctorado en
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, climate economics, operations research, or statistics); • Proficiency in at least one scientific programming language (e.g. Julia, Python, etc) required; • Experience with numerical models (preferably
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Engineering, Electrical Engineering, or a related field. Experience in robotic perception, control, or learning-based methods. Proficiency in Python and C++ for AI and robotics applications. Familiarity with
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Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry Classification Minimum Requirements: PhD in Chemistry or related area Strong coding (Python) and
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; prior coding experience (e.g., Python, MATLAB, or similar) is a desirable plus but not strictly required. Experience with mouse surgery is highly desirable. Track record of productivity, independence, and
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interatomic potential development. Proficiency in scientific programming (Python and/or Fortran). English proficiency is also required. Desirable requirements: Experience with machine-learning interatomic
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 9 days ago
proficiency in scientific programming (Python or R) and deep learning frameworks (PyTorch or TensorFlow) is recommended, alongside experience processing large spatial datasets using cloud or high-performance