Hiroki Kozuki (hiroki-kozuki)

hiroki-kozuki

Geek Repo

Company:Imperial College London

Location:London, United Kingdom

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Hiroki Kozuki's repositories

Calculating-the-Hubble-Constant

A 1st year computing project for the undergraduate physics course at Imperial College London.

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ossu-computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

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1st-Year-Labs

Compilation of codes written for data analysis in the 1st year practical lab sessions of the undergraduate physics course at Imperial College London.

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2nd-Year-Labs

Pieces of code from the 2nd Year Advanced Practical Physics module for the Physics course at Imperial College London.

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Data-Science-Soc

Exercises and resources from the Imperial College London Data Science Society.

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FTS-Analysis-of-Fe-II-Spectra-for-Astrophysics

Research Project for Imperial College London's Undergraduate Research Opportunities Programme. Aiming to confirm new energy levels of Fe II by analysing laboratory data and matching results to the observed spectra of the HR 6000 star.

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Neutrinos-from-Pion-and-Kaon-decays_Debugging-exercise

An individual Python debugging exercise for the Mechanics and Relativity module of the undergraduate physics course at Imperial College London

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STOM-Higgs-Boson-Mass-Data-Analysis

A 1st year statistics of measurements project for the undergraduate physics course at Imperial College London.

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Thermionically-Emitted-Electron-Beam-from-Thoriated-Tungsten

A 1st year Summer group project for the undergraduate physics course at Imperial College London.

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Thermodynamic-Simulation

A 2nd year computing project for the undergraduate physics course at Imperial College London.

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hiroki-kozuki

Config files for my GitHub profile

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hiroki-kozuki.github.io

A personal website based on the al-folio template from Jekyll themes, RubyGems

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Lecture-series-2022-23

Materials used for Lecture series done in 2022-23 academic year

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ossu-data-science

:bar_chart: Path to a free self-taught education in Data Science!

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pytorch-deep-learning

Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.

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