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Field
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to identify and model the most efficient catalytic sites on NDs using advanced Density Functional Theory (DFT) calculations. The project seeks to revolutionize the design of ND-based catalysts by controlling
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 14 days ago
the prediction of vehicle flows, energy demand, and flexibility of electric vehicle fleets, with applications to energy and transportation systems. The work lies at the intersection of systems and control, data
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-control vs cohort, etc.). Strong grasp of statistical/ epidemiological principles e.g. risk prediction and survival/time-to-event modelling. Experience with uncertainty aware evaluation: confidence
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. Preferred Qualifications Experience in development of predictive video display systems Experience in teleoperation and/or control of autonomous vehicles Experience in working with sensor systems such as GNSS
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-identification, and version control. • Collaborate with clinicians, engineers, and data scientists to integrate imaging biomarkers into predictive and diagnostic models. • Contribute to study design, IRB
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-identification, and version control. • Collaborate with clinicians, engineers, and data scientists to integrate imaging biomarkers into predictive and diagnostic models. • Contribute to study design, IRB
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drug residence time, creating a high burden for patients and clinics. We thus aim to develop a minimally invasive, injectable biomaterial platform designed to enable predictable, long-term ocular
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, predictive models), AI‑driven network functions (closed-loop control, optimization, anomaly detection, intent resolution). You are familiar with cloud-native development (Docker, Kubernetes), CI/CD pipelines
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predictive control of carbon mineralisation through high-throughput mineralogy and machine learning.” This is an exciting opportunity to contribute to innovative research at the interface of mineralogy
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of artificial intelligence, multi-omics data integration, and functional genomics, aimed at predicting synthetic lethality in cancer - including representation learning, nonlinear embeddings, and predictive