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with scientific programming (e.g., MATLAB, Python) and image analysis software (e.g., FIJI/ImageJ). Excellent communication and interpersonal skills. Strong analytical thinking, problem-solving skills
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productivity from previous training • Excellent oral and written communication skills are mandatory • Expert knowledge in scientific computing, including fluency in MATLAB and Python • Advanced skills with Linux
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Python and GEE for geospatial analysis and data science. Experience with machine learning/deep learning frameworks (e.g., PyTorch, TensorFlow) applied to image or geospatial data. A track record of first
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, biostatistics, biomedical informatics or related fields OR a PhD degree in biology or related fields. Preferred Qualifications: A strong background in programming (e.g., Python or R or C++) is essential. Research
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written communication skills, be strongly motivated, and work well independently as well as in a team setting. Experience with computer programming is a plus (MATLAB, Python, and/or E-Prime). Special
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or organoids. Basic bioinformatic knowledge (R/Python) or ability to learn. Excellent oral communication and written skills in English, with good organization, attention to detail, and analytical abilities
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, pharmacology, engineering, bioinformatics, or a related field. Preferred: Prior pharmacokinetic modeling or machine learning experience using software such as R, Python, MATLAB, GastroPlus, PK-Sim, Phoenix
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data analysis. Record of scholarly publications and scientific communication skills Proficiency in statistical and data analysis tools (e.g., MATLAB, Python, R). Preferred Qualifications: Experience with
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knowledge (R, Python, or other programming languages) or ability to learn. Excellent oral communication and written skills in English, with good organization, attention to detail, and analytical abilities
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, Freesurfer, UNIX/LINUX computational environments and/or programming skills (MATLAB, R, C++; JAVA, Python) is desirable, but not required. Applicants who have experience in the study of children (especially