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Frankfort, KY Grade Level 50 Salary Range $81,640-138,798/year Type of Position Staff Position Time Status Full-Time Required Education MA Click here for more information about equivalencies: https
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, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job Responsibilities
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development, in particular with courses on condensed matter physics, quantum materials and muon science, and supervising PhD students as well as Bachelor and Master theses. The successful candidate will head
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: Required: • MSc (or equivalent) in: Computer Science, Cybersecurity, Machine Learning, or related field • Strong background in: machine learning / deep learning, mathematics (probability, linear algebra
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IFREMER - Institut Français de Recherche pour l'Exploitation de la MER | Sete, Languedoc Roussillon | France | 8 days ago
Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 20 Apr 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Is the job funded through
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Engineer V - Electrical and Computer Engineering (ECE) Posting Number req25745 Department Electrical and Computer Engr Department Website Link https://engineering.arizona.edu/majors/electricalcomputer
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the development, evaluation, and practical application of machine learning methods (especially deep learning) Publication of scientific results in renowned international journals and conferences Teaching of courses
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students. The rest of your time (40%) is devoted to teaching. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning and AI methods
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deep experience with PyTorch, JAX, or TensorFlow. Broad knowledge of modern ML and optimization (gradient‑based, evolutionary, Bayesian, reinforcement learning). Hands‑on experience with generative
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how