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Field
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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
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, physics, or a medical imaging related field. Experience with developing advanced pulse sequences or accelerated acquisition and reconstruction algorithms will be highly valued. Interested candidates should
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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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energy, personalized medicine and climate science and we need your help to develop tools and workflows to drive exploitation of this world leading resource. You will also play a key role in fostering a
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environment empowering our people to fulfil their personal and professional ambitions Gender-friendly environment with multiple actions to attract, develop and retain women in science 32 days’ paid annual leave
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aimed at breakthrough innovations in model capabilities, reasoning, safety, and efficiency. Algorithm & Model Development (LLMs & AIGC): Design and develop novel architectures for large-scale language and
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focus will be on developing compact, efficient, and real-time LLM algorithms/hardware on the edge and developing demos for specific applications such as speech disorder therapy. Your responsibilities will
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on the littoral environment. Algorithm development includes photogrammetric measurements of wave parameters, image stabilization, and use of AI/ML models for image segmentation and classification. Algorithms will
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health-related data, with a focus on longitudinal data analysis, population databases, and causal inference. Responsibilities Data Cleaning and Preparation: Clean, validate, and prepare datasets