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in laboratory medicine. The ideal candidate has the quantitative and computational background to analyze sequencing, high dimensionality data, and be efficient with bash, python, and/or R. Expertise
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Biological & Life Sciences Division – Postdoc Researcher: AI for Precision Medicine and Therapeutics
research (e.g., Python, TensorFlow, PyTorch). Record of publications in top-tier conferences or journals in bioinformatics, AI, or related fields. Strong problem-solving and analytical skills. Excellent
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platforms Contribute to research proposals and peer-reviewed publications Primary Platforms May Include ArcGIS Pro CityEngine Python-based geospatial processing ecosystems Cesium and 3D Tiles
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, oscillation, response-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB , R, or related computational environments
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related discipline, with experience in field methods and/or environmental data analysis; familiarity with Python-based workflows, AI/ML, or hydrogeophysical modeling is highly desirable. The position
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knowledge, such as bonding, defects, catalysis, batteries, solid-state chemistry, molecular systems, or related materials classes. Strong Python skills and familiarity with LLM APIs, agent frameworks
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-effects models, survival analysis, dimensionality reduction, clustering, trajectory modeling) Lead reproducible analysis pipelines in R, Python, or related frameworks Interpret results in biological and
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experience in geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
experience in geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement
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du scanner 3D développé en tant que preuve de concept par le laboratoire (principe physique d'acquisition, format des données en sorties, interfaçage python, etc…). Par la suite, la démarche