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programming languages such as R or Python Experience with multiome data analysis (e.g. methylomics, Lipidomics, Proteomics, ATAC-seq) Proven experience with advanced computational methods such as deep and
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(e.g. fuel cells, batteries, heterogeneous catalysts, interfaces) Proficiency in high-level programming languages, e.g. Python Previous supervision of graduate students, as well as laboratory
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. Proficiency in a scientific programming language (Python preferred). Track record of peer-reviewed publications. Strong organizational and teamwork skills. Online Application Required Documents Curriculum Vita
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(semantics/prosody preferred). Experience with neuroimaging (e.g., fMRI) and quantitative analysis. Proficiency in Python/MATLAB or similar tools. Record of peer-reviewed publications and independent research
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publication record in top-tier venues: ICRA, IROS, RA-L, T-RO, IJRR, CoRL, NeurIPS, RSS, CDC, TAC. Proficiency in Python, C++, ROS, and machine learning frameworks such as PyTorch or TensorFlow. Excellent
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processing, and user-friendly GUI-based analyses for clinical and physiological research. You will combine Python-based software development, biomedical signal processing, and FAIR data design, and contribute
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
system and its components. ISSM is a modular C++ codebase with MATLAB and Python interfaces, designed to simulate Cryosphere, Solid Earth, and Sea Level processes, either in coupled or standalone
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programming skills (e.g., C, C++, Python), familiarity with Linux Proficiency in English and ability to work in a team Outstanding analytical and problem-solving skills Employment terms: The successful
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in programming, ideally with experience in languages commonly used in computational biology (e.g. Python, C). Experience with HPC, workflow managers (e.g. Snakemake, Nextflow), and containerization
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novel machine-learning methodologies Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices Experience training and deploying machine-learning