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at the intersection of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning
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command of data wrangling, cleaning, and large-scale dataset management. Machine Learning/Deep Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation
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human cell systems, and in vivo models. We routinely work with ex vivo primary cells and patient-derived specimens and employ humanized mouse models to enable mechanistic studies and forward genetic
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learning university. In this competition, experience in research and/or development in Large Language Models (LLM) and their respective applications is valued. Where to apply Website https
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perspective, including (but not limited to): machine learning and statistical learning computer vision and sensor-based data analysis natural language processing and large language models hybrid, interpretable
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biofoundries, fostering sustainable industrial biotechnology across the bioeconomy sectors. The lab combines synthetic biology, machine learning, and biosensor and pathway dynamic regulation design to produce
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g
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of machine learning What We Offer: On the basis of full-time employment (40 hours/week) the minimum salary in accordance with the collective agreement is € 5,014.30 gross per month (14 x per year, CA Job Grade
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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning