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. The successful candidate will contribute to the development of predictive numerical models and experimentally validated designs for intricate mechanical assemblies, deployable structures, compliant mechanisms, and
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tools, such as physics-informed climate and weather predictive models, and trustworthy datasets for training and analysis. Its work aims to improve prediction capabilities and understanding of climate
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Details A brain decoding model aims at predicting what sensory stimulus is received (e.g. visual stimuli, different images), which mental state is experienced (i.e. asleep, awake, drowsy) or even what is
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. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS CASC. Qualifications Required Qualifications: A completed
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. Combining AI-based prediction (e.g., TCNN, LSTM, etc) with musculoskeletal models to estimate and predict muscle activation and tendon force over short horizons (e.g. ~200 ms). Integrating these predictions
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transcriptomics and multi‑omics data. You will also partner with AI experts to integrate predictive models and advanced analytics into omics workflows. You will work in an expanding team led by Dr. Masoomeh
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impact-based health early warning systems. The successful candidate will join the research team of Dr. Joan Ballester Claramunt (https://www.joanballester.eu/ ) at ISGlobal within the framework
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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background in nonlinear optics, ultrafast photonics, and integrated photonics, alongside the ability to develop predictive models for optical materials and photonic devices. The successful candidate will work
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, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating model predictions with biological knowledge and external data sources. Work closely with academic partner