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physical models (including dispersion forces, magnetic effects, and ligand–solvent interactions), and train modern deep-learning methods to create smooth and reliable energy landscapes. A key goal is predict
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Architecture Search (NAS) that can automatically design efficient deep learning models optimized for specific embedded hardware platforms. These models will be deployed in resource-constrained, standalone
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PhD in in a relevant field would be an advantage. Deep understanding of the needs of a diverse student population, including students from equity backgrounds, and contemporary issues impacting equitable
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staff working in the R&D unit. Where to apply Website https://jobs.energy.imdea.org/es/offer/394 Requirements Research FieldEngineering » OtherEducation LevelPhD or equivalent Skills/Qualifications PhD in
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challenging and impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. We have a collaborative environment focusing on designing
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with artificial intelligence (machine learning/deep learning) Essential Application/interview Experience with classical image processing techniques (e.g. classification/segmentation/registration
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infrastructure (e.g., turbine components). Research on advanced deep learning techniques, including architectures based on GRU, LSTM, attention mechanisms, and hybrid models. Implementation of real-time predictive
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the undergraduate and graduate levels. Where to apply Website https://www.bth.se/english/vacancies/job/phd-student-position-in-software-engin… Requirements Research FieldComputer science » Computer systemsEducation
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algorithms as well as deep learning workflows on GPU servers (use of Git, Docker, and PyTorch) Design, implementation, and evaluation of spatial proteomics and multiplex analyses for characterizing the tumor
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field between fundamental research and technological development. You enjoy supervising and teaching and using your excellent communication skills to inspire the coming generation develop the same deep