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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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Python, MATLAB, or C++. Experience with machine learning techniques, CAD, computational modeling, 3D printing, motion capture, and/or material testing. Proficiency in programming languages commonly used in
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proficiency in Python and/or R; familiarity with working in a Linux/Unix environment; fluency in English (both written and spoken); excellent communication and teamwork skills in an interdisciplinary
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pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings. Coordinate the collaboration between the chronobiology lab (led by Dr. Paul Petrus) and the
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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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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Materials A single PDF or Word document containing a resume, letter of interest, and a list of three (3) professional references is required to apply. Important Instructions: When prompted to upload your
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(e.g., python, tcl) The candidate will also help with mentorship and training graduate and undergraduate students. The terms of employment, which would be a year, with a possibility of renewal, include a
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interpretation through deep learning model development using Python and MATLAB. Why should I apply? Under the guidance of a mentor, you will engage in a variety of research activities, including: developing
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advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python, R, and Unix/Linux