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Project title: Developing and Evaluating Interpretable Approaches for Human-Centered Machine Learning.Project description: Approaches in interpretable machine learning offer promise in understanding
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tools such as LTSpice (preferred)/TINA/Multisim. Knowledge of low-power biopotential amplifier design and energy harvesting techniques is preferred. Candidates with machine learning skills, particularly
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capacity to learn technical skills, then perform the same tasks independently, reproducibly • Evidence of highly detailed, accurate record keeping • Excellent time management, written and
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examining the key personal, educational, professional and systemic influences that define and shape their early careers and practice, including the impact of different learning and professional development
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Psychology at Trinity College. They will participate fully in lab life, attending regular meetings and collaborating with other lab members. They are expected to be a self-starter, eager to learn and
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or equivalent) Experience with machine learning tools (e.g., Scikit-learn, Tensorflow, PyTorch, etc.) Track record of publications/and conference papers in software modelling or programming, or system level
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environment where diversity is celebrated. As a University we strive to create a workplace that reflects the diversity of our student population where people from a wide variety of backgrounds learn from one
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job market. The School of Theology, Philosophy and Music is a dynamic and creative learning environment with a strong commitment to social and cultural engagement, and world-class research. The academic
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hyperspectral data acquired from outcrops, drill cores and UAVs into a single 3-D model that, with the aid of machine learning protocols, can be upscaled to facilitate regional mineral exploration strategies in