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platforms. Experience in development of digital twins or physics-informed machine learning models. Experience in programming (e.g., Python or equivalent) and development of control or data acquisition
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means contributing to a better tomorrow. Whether you are a current resident of our New Haven-based community, eligible for opportunities through the New Haven Hiring Initiative, or a newcomer, interested
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regionalization and lamination of the human cortex and their deregulation in disease using different in vitro models derived from human pluripotent stem cells. The successful candidate will be in charge of
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intelligence (e.g., reinforcement learning for scheduling, congestion control, link adaptation, and cross-layer optimization). The exact research direction will be refined based on the candidate’s background and
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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g
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design strategies, while producing structured spatio-temporal datasets that will serve as input for realising predictive models. Objective 3 — Realize predictive tools for scenario-based assessment
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vary based on the position and may include: Medical, prescription drug, and dental coverage Paid vacation, holidays, and various leave programs Competitive retirement benefits, including defined
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of identity resolution concepts Familiarity with data quality frameworks and reconciliation patterns Knowledge of SCD Type 2 and historical data tracking Experience with Git-based version control and CI/CD
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models and transformer-based architectures to construct high-dimensional design spaces. These models are integrated with Deep Reinforcement Learning (DRL) for fine-tuning or end-to-end learning, enabling
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Research Associate to join a research team advancing the modelling, control, and real‑time validation of power‑electronics‑dominated power systems. This role supports the ARC Discovery Project “Making weak