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Field
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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quantitative biomedical sciences. Experience with R and/or Python packages Experience working with Linux, including shell scripting Preferred Qualifications: PhD or MD/PhD degree in Bioinformatics, AI machine
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for those who can also program in Python and know standard network analysis techniques and metrics such as community detection and centralities. Prefernce for those with experience in geo-spatial analysis
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understanding, or digital twins • Experience working with real-world and synthetic datasets and sensors (e.g., RGB, depth, LiDAR) • Proficiency in Python and frameworks such as PyTorch, OpenCV, or Open3D • US
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Duties Designs and conducts histology and imaging analysis in animal models. Uses computational tools (R, Python, BLAST) for data analysis. Writes research papers for peer-reviewed journals. Presents
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, biomedical engineering, or a related field. • Experience with coding software such as Python, R, or Matlab. • Familiarity with collaborative coding software such as GitHub and database management
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linear, mixed-integer, and stochastic programming. Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis. Collaborate with
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for data acquisition and analysis (e.g., MATLAB, Python, Prism/GraphPad, EEG scoring software). Strong organizational, written, and oral communication skills. Ability to work independently and
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 hours ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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is necessary for career entry. Preferred Proficiency in UNIX shell, Python, or other programming languages is preferred. Experience handling large next-gen sequence datasets is preferred. A