63 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" research jobs at Texas A&M University
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); Proficiency in machine learning and Python programming; Strong scholarly writing skills with a demonstrated publication record and fluency in LaTeX compilers; Excellent verbal and written communication
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Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques. Experience with wearable and sensors placed in the environment
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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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driving simulators (e.g., CARLA, SUMO) or robotic simulation environments (e.g., Isaac Sim, Gazebo). Experience with ROS or ROS 2. Experience with PyTorch or other machine learning frameworks. Hands
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one or more of the following: Python, SAS, STATA, Matlab, or related statistical packages. Strong attention to detail and high ethical standards. Willingness to learn and acquire new skills as needed. A
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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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Staff Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences
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, Texas Job Type Staff Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents
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languages such as R, Python, Matlab. Familiarity with software development best practices (e.g., unit testing, version control). Familiarity with inferential statistics and machine learning. Expertise in