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Field
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skills in statistical analysis, i.e.. R Studio, SAS along with experience in coding, i.e. MATLAP, Python, etc. Salary Range $61,008+ depending on NIH level Working Conditions Standard office environment
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with omics data analysis, biostatistics, and image analysis tools. Strong programming skills (R, Python) and knowledge of relevant databases and pipelines. Candidates with peer-reviewed publications
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with computational environments for ’omics data manipulation (command line, Python, R, etc.) * Deep knowledge in at least one relevant subdiscipline, i.e. bioinformatics, microbiology, microbial ecology
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. Basic Qualifications An ideal candidate will have a PhD in computational biology/bioinformatics/statistics/CS or another quantitative field, as well as superb programming (Python, shell scripting) and
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scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage
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computational research projects is required for the proposed research. Expertise in population or evolutionary genetics is preferred but not required. Required skills: · Experience with Python · Experience with
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Center for Devices and Radiological Health (CDRH) | Silver Spring, Maryland | United States | about 16 hours ago
., Python, g-code) Data analysis Statistical analyses How to create reports Mentor: The mentor for this opportunity is Prasanna Hariharan ([email protected] ). If you have questions about the
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optical systems and the use of lasers. Proficiency in data processing software e.g., Python, Matlab. Exhibits excellent professionalism and work ethics, initiative and self-motivation. Strong ability
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 16 hours ago
on the development of optical models, image acquisition software, image processing algorithms, and deep learning algorithms using Zemax, Python, Matlab, LabVIEW, CUDA, and other programming environments. Training
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with healthcare data (e.g., EHR, clinical text, imaging, omics). Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases