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Field
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant neuroscience community, and there are opportunities
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Documented expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills
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evolution and protein optimisation, applying molecular biology and biophysics. Researchers will be supported to develop skills in the latest AI or machine learning tools for protein design. According
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Statistical Signal Processing, Data Science, Machine Learning with an interest in astrophysics - or a PhD in Astroparticle Physics with skills and professional experience in experimental data analysis. Website
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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or equivalent Research FieldEngineering » OtherEducation LevelPhD or equivalent Skills/Qualifications Skills in acoustics (PhD in acoustics required) and acoustics software. Skills in machine learning and deep
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Teach and supervise at undergraduate, master’s, and PhD levels Collaborate with academic and industrial partners Disseminate results in top-tier conferences and journals Requirements PhD in
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge