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Hours of work: Full-time Tenure: Fixed term for 1 year We are seeking to appoint five outstanding postdoctoral research associates with expertise in the synthesis and characterisation of new inorganic, hybrid and core-shell materials, with applications in...
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Details Title Postdoctoral Fellow in Computer Science — From Theory to Practice: Reinforcement Learning for Large Scale Foundation Model Post‑Training School Harvard John A. Paulson School of
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on psychological assessment of learning and attention disorders and neuropsychological testing with youth with various medical conditions (including seizures, cancer, and kidney transplant patients). The fellows
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companies. Hybrid & Data-Driven Modeling: Apply machine learning and hybrid physics-AI approaches to model industrial systems, accounting for physical constraints, sensor noise, and heterogeneous datasets
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contributing effectively in a collaborative and multidisciplinary research environment. Education Requirement: PhD, MD/PhD or MD with significant bench research experience. Required Qualifications: Good writing
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Department:Urology Salary Salary:Commensurate Position Details Full/Part Time Status:Full Time Percent Time:100% Position Description: University of Iowa Health Care Department of Urology seeks a highly motivated Post
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for multimodal brain-circuit analyses. Lead manuscripts and conference presentations, and contribute to grant applications and new research directions. Education Requirement: PhD and/or MD in neuroscience
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various levels of expertise. Education Requirement: A PhD or MD/PhD degree in Health Sciences or a related field. Required Qualifications: Expertise in cancer biology, with at least 2 years of experience
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SD-26109- POSTDOCTORAL RESEARCHER IN AI-BASED ENERGY MANAGEMENT OF RESILIENT MICROGRIDS WITH SECO...
systems and microgrids - Battery energy management systems - Renewable energy integration - Control systems - Artificial intelligence or machine learning for energy applications Experience and skills
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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology