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demonstrating values-based leadership that supports wellbeing, inclusion, and organisational excellence. This is a research focused position. Further information can be found by viewing UQ’s Criteria for Academic
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for Big Data Research in Health (CBDRH), strategically partnered with the School of Clinical Medicine (Women’s Health) at the University of New South Wales, Sydney (UNSW). This positioning places the NPESU
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Observatory, and LSST. Expertise in modeling cosmological observables through analytical methods or numerical simulations, machine learning, statistical inference, and data reduction is beneficial. As members
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electrical/magnetic testing Micromagnetic Simulation and physical modeling Mentoring of PhD and Masters/undergraduate students Job Requirements: The candidate should have a PhD in Physics. The candidate should
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to compare the genetic architecture of rare vs. common genetic variation 2. Developing methods to analyze GWAS data using graphical models and genome-wide genealogies 3. Developing methods to integrate rare
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» Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 25 Nov 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per
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5 Nov 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Portugal
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 24 Nov 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5
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ultrasound sensors High level analytical capability Ability to communicate complex information clearly Fluency in relevant models, techniques or methods and ability to contribute to developing new ones, in
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, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate observations/models and from models