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methods using statistics, machine learning, generative AI, and modern software approaches. These methods will be developed to pilot and evaluate new interventions in partnership with government agencies
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models
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one or more of: computational modeling of social learning, norms, or moral cognition; cultural evolution and gene-culture coevolution; evolutionary game theory and the evolution of cooperation
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant
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/hacohen19a.pdf [4] Roh et al., FairBatch: Batch Selection for Model Fairness — https://arxiv.org/pdf/2012.01696 [5] Ren et al., Learning to Reweight Examples for Robust Deep Learning — https://arxiv.org/pdf
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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines
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single‑cell omics, AI machine learning, and translational biology. The role involves collaboration with academic research group(s), with a strong focus on bridging advanced computational methods
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scientific methods, often in conjunction, including qualitative data collection and analysis via semi-structured interviews, structural micro-econometric modeling and estimation, and statistical analysis
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 25 days ago
. The candidate(s) may also be required to apply data fitting algorithms/machine learning algorithms to link models to biological data from the literature. The project integrates elements from dynamical systems