<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Jan Kieseler | PHINDER EIC Project</title><link>https://phinder-eic.github.io/author/jan-kieseler/</link><atom:link href="https://phinder-eic.github.io/author/jan-kieseler/index.xml" rel="self" type="application/rss+xml"/><description>Jan Kieseler</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://phinder-eic.github.io/media/icon_hu_ebbff252c19052d0.png</url><title>Jan Kieseler</title><link>https://phinder-eic.github.io/author/jan-kieseler/</link></image><item><title>On the Codesign of Scientific Experiments and Industrial Systems</title><link>https://phinder-eic.github.io/publication/dorigo-2026-codesignscientificexperimentsindustrial/</link><pubDate>Thu, 01 Jan 2026 00:00:00 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+0000</pubDate><guid>https://phinder-eic.github.io/publication/de-vita-2025/</guid><description/></item><item><title>End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters</title><link>https://phinder-eic.github.io/publication/schmidt-2025-endtoenddetectoroptimizationdiffusion/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/schmidt-2025-endtoenddetectoroptimizationdiffusion/</guid><description/></item><item><title>End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters</title><link>https://phinder-eic.github.io/publication/particles-8020047/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/particles-8020047/</guid><description/></item><item><title>Hadron Identification Prospects with Granular Calorimeters</title><link>https://phinder-eic.github.io/publication/particles-8020058/</link><pubDate>Wed, 01 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+0000</pubDate><guid>https://phinder-eic.github.io/publication/kieseler-2022/</guid><description/></item><item><title>Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm</title><link>https://phinder-eic.github.io/publication/dorigo-2022-deepregressionmuonenergy/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/dorigo-2022-deepregressionmuonenergy/</guid><description/></item><item><title>Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks</title><link>https://phinder-eic.github.io/publication/neub-ser-2022/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/neub-ser-2022/</guid><description/></item><item><title>Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper</title><link>https://phinder-eic.github.io/publication/dorigo-2022-endtoendoptimizationparticlephysics/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/dorigo-2022-endtoendoptimizationparticlephysics/</guid><description/></item><item><title>Toward Machine Learning Optimization of Experimental Design</title><link>https://phinder-eic.github.io/publication/baydin-02012021/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/baydin-02012021/</guid><description/></item><item><title>Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data</title><link>https://phinder-eic.github.io/publication/kieseler-2020/</link><pubDate>Tue, 01 Sep 2020 00:00:00 +0000</pubDate><guid>https://phinder-eic.github.io/publication/kieseler-2020/</guid><description/></item><item><title>Muon Energy Measurement from Radiative Losses in a Calorimeter 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