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collaboratively within an interdisciplinary research environment. Desirable experience with advanced AI or machine-learning methods beyond standard predictive modelling prior exposure to qualitative or mixed
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model we deploy and support the ecosystem with training, advisory, and AI infrastructure. CeADAR is seeking a data scientist with solid experience in machine and deep learning research and development
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Newton`s method. In
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an open working model that combines remote working with work on-site, according to organizational needs and the nature of the tasks involved. About the UOC A leader in e-learning, our pioneering
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are dedicated to student learning and success. The Board recognizes that diversity in the academic environment fosters awareness, promotes mutual understanding and respect, and provides suitable role models
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scoping to deployment and monitoring of production-grade models—with a focus on both Generative AI and Deep Learning. The ideal candidate holds a Ph.D. in Deep Learning or Generative AI and brings a strong
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AI for Science and Engineering and Foundation Models and Generative AI axes. The recruited candidate will develop contributions at the interface between machine learning, optimization and quantum
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at finale.seas.harvard.edu and our group’s webpage https://dtak.github.io/ We work on probabilistic models, reinforcement learning, and interpretability + human factors. Basic Qualifications Candidates are required to have
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners
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adaptation of pre-trained microscopy vision models and cross-modality representation learning/ alignment. You will build robust pipelines that adapt foundation models to specialized microscopy tasks and