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position as Senior Lecturer. Optimization, machine learning, and control theory together form a central toolbox for understanding, analyzing, and controlling complex systems. These fields span deep
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. The candidates will be working with the QV2 software team and will learn to develop scientific software in a collaborative environment. Project 1: Readout Optimization and Software Development This project focuses
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programming, one in optimization, and one in machine learning at least one advanced-level course in stochastic processes, or in related subjects such as time series analysis, spatial statistics, spectral
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modelling, numerical methods for multiphysics problems, and the development of numerical optimization methods for virtual design of metamaterials and structures. Do you want to be part of a dynamic research
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directions of optimization of device performance. About us The Microwave Electronics Laboratory (MEL) at the Department of Microtechnology and Nanoscience employs more than 40 people performing world-class
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, numerical methods for multiphysics problems, and the development of numerical optimization methods for virtual design of metamaterials and structures. Do you want to be part of a dynamic research environment
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Giacomello. Examples of tasks: Design, perform, and optimize experimental workflows for ST, SmT and single-cell multiomics Prepare and process animal and plant tissue samples for spatial and sequencing-based
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the EU’s ambitious AI Factories initiative. Learn more: https://mimer-ai.eu/about-mimer/ , https://www.naiss.se , https://eurohpc-ju.europa.eu/ai-factories_en The position Do you want to work at the
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microscopy on magnetic materials and/or the use and development of coherent x-ray microscopy techniques, to join the SoftiMAX team. As part of the team, you will ensure optimal operation of the beamline plus
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at KTH Royal Institute of Technology is seeking a highly motivated doctoral student with a strong background and interest in spatial AI, navigation, sensor fusion, optimal estimation, and machine learning