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Field
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Science, or related field Knowledge and experience in computer vision, machine learning, and deep learning Good written and oral communication skills Experience in leading research projects Proficiency in basics
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combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the globe. Candidates with
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questions in cancer biology. Learn more about Dr. Bild’s lab here. As a successful candidate you will: Build probabilistic models of tumor dynamics from serial ctDNA and tissue samples. Develop deep learning
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verbal, written communication skills In-depth knowledge of deep learning, specifically VLM models, computer vision techniques (e.g., open vocabulary object detection, model distillation, VQA, test-time
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Daphné Lemasquerier conducting research for a UKRI Future Leaders Fellowship (FLF) funded project entitled “Fluid Dynamics of Deep Interiors in the Outer Solar System”. The primary duties will be
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metabolism in ageing models. The use of AI and Deep Learning for data analysis and the establishment of in vitro ageing experimental systems and factor isolation using CRISPR Screening are also planned
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, and multimodal deep learning approaches that enable privacy-preserving learning across distributed healthcare data sources Support health outcomes with research in transformative applications in
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related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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. Willingness to support project reporting and milestone reviews. Hard skills Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Experience with reinforcement learning