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Field
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Experience with machine/deep learning / AI applied to environmental or urban systems Familiarity with climate modeling, urban climate, urban agriculture, water resources, and energy systems Experience working
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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the form of papers, posters or oral presentations Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow Downloading a copy of our Job Description Full details
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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mobilities, driving innovative contributions to the field. Supervise students on Bachelor, Master and PhD level and independently teach courses as outlined in the collective agreement, mentoring the next
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Relativity, and cosmological measurements using GWs such as Hubble constant and probes of inflation and phase transitions in the early universe. We are developing new data analysis methods like the use of deep
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 3 months ago
"Cloud physics" (f/d/m/x) Background With the project Deepcloud, we will use the machine-learning revolution to better understand clouds and their role in the climate system. We aim to train a deep
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in digital health, fitness, and rehabilitation. Preferred Qualifications Background in machine learning/deep learning is a plus. Equipment Utilized Physical Demands and Work Environment PHYSICAL
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from the areas of few-shot learning, continual learning and modular deep learning, as well as different LLM alignment frameworks, based on reinforcement learning and direct preference optimisation