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degree in computer science 2 years of experience in machine learning research and development Strong background in generative AI Expertise in deep learning, generative AI, reinforcement learning
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Liquid Argon Calorimeter system. Use novel high-level synthesis approaches developed internally (based on functional programming abstractions), to optimize the implementation of machine learning models and
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: Experienced in applying machine learning and data science tools (TensorFlow, PyTorch, pandas, NumPy, Scikit‑learn) to analyze and model complex atmospheric and environmental datasets. Proficient in multi
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style Attention to detail Clear oral and written communication skills Experience and Education BSc in a related field with relevant coursework 1-2 years additional experience Background in NLP, machine
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biostatistics and epidemiology Expertise in quasi-experimental, econometric methods and other advanced methods (e.g., longitudinal data analysis, trial emulation, interrupted time series, machine learning
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of Professor Odile Liboiron-Ladouceur, the Research Assistant 2 conducts independent and collaborative research in computational imaging and photonics, with a focus on machine learning based 3D reconstruction
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and revise existing IMAGE machine‑learning components to optimize efficiency, scalability, and quality of results. Implement conversions of existing non‑LLM components to LLM‑based approaches where
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networks, machine learning, Markov models and other computational approaches to biology are important. Familiar with analysis and data visualization. Enrolled in an undergraduate degree. Location
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline