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designing and benchmarking integration algorithms. The position is situated in the Intelligent Data Engineering Lab and will be supervised by Dr. Jan-Christoph Kalo and Prof. Paul Groth. What you will do
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. Numerical Analysis and Scientific Computing, developing numerical methods and algorithms that are accurate, efficient, and robust with respect to challenges that manifest in the simulation of complex
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of information in their daily lives - from local news to institutional messaging and algorithmically curated feeds. We are investigating how trust forms, erodes, and gets repaired, and how technologies
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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between
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executing the experiments Developing relevant algorithms for experimental data post-processing Mentor and work closely with multiple Ph.D. and M.S. students as well as four principal investigators from
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algorithms using Monte Carlo simulation and Bayesian inference to distinguish normal tritium losses from suspicious discrepancies during transport, and to develop statistical thresholds that balance detection
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hypothesis testing to assess the validity of a set of candidate thresholds. Therefore, the internship position aims to extend this work by exploring one or multiple of the following directions of improvement
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, that will drive research and innovations at multiple levels - from developing scalable quantum processor technologies to solutions for the quantum-classical control and readout interface, and all the way
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algorithms, analysis pipelines, and software in order to establish and ensure effectiveness and scalability of computing infrastructure. Provides consultation for and expertise with computer applications to
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frameworks such as COLMAP and Open3D; Ability to develop algorithms for object detection and tracking, 3D reconstruction, and SLAM. Advanced Control and Intelligent RoboticsSolid knowledge of classical and