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mikraemer.bsky.social
Michael Krämer
@mikraemer.bsky.social
140 followers 130 following 4 posts
Physicist at RWTH Aachen. Interested in particle physics, astroparticle physics, cosmology, machine learning, philosophy of science.
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lmu-mcmp.bsky.social
Munich Center for Mathematical Philosophy @lmu-mcmp.bsky.social · Jan 18
Three-year postdoctoral fellowship opening at the MCMP; please help us spread the word.
erc-rescale.bsky.social
ERC project RESCALE @erc-rescale.bsky.social · Jan 17
I’m hiring a postdoctoral fellow for 3 years to work on the philosophy of physics at the Munich Center for Mathematical Philosophy (MCMP), LMU Munich (deadline: February 23, 2025). More information below. @lmu-mcmp.bsky.social @lmuphilosophy.bsky.social
Postdoctoral Researcher (m/f/x)
job-portal.lmu.de
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Reposted by Michael Krämer
kasieczka.bsky.social
Gregor Kasieczka @kasieczka.bsky.social · Jan 17
Interview (in German) I recently gave to the FAZ Vor:Denker on AI, Physics, and how they do & will benefit each other.

vordenker.faz.net/protonen-hab...
»Protonen haben keine Rechte« | vor:denker
Wenn die Teilchenphysiker in der Nähe von Genf 40 Millionen Kollisionen pro Sekunde erzeugen, sind sich Physik und Künstliche Intelligenz ganz nah. Dann generiert der Teilchenbeschleuniger des Europäi...
vordenker.faz.net
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mikraemer.bsky.social
Michael Krämer @mikraemer.bsky.social · Jan 10
With @henkderegt.bsky.social, @danielkostic.bsky.social, @kasieczka.bsky.social and others, we have submitted a white paper that provides a potential roadmap for the development and evaluation of physics-specific AI models: arxiv.org/abs/2501.05382, see also www.lorentzcenter.nl/physics-en-q....
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based ...
arxiv.org
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mikraemer.bsky.social
Michael Krämer @mikraemer.bsky.social · Jan 6
We also provide the AspenOpenJets dataset for further public use in ML applications: www.fdr.uni-hamburg.de/record/16505
Aspen Open Jets: a real-world ML-ready dataset for jet physics
This dataset contains approximately 180 M boosted jets, derived from open data collected by the CMS experiment at the Large Hadron Collider (LHC) in 2016 — specifically the JetHT datastream — and pres...
www.fdr.uni-hamburg.de
mikraemer.bsky.social
Michael Krämer @mikraemer.bsky.social · Jan 6
Foundation models play an increasingly important role in physics. With @ozamram.bsky.social @joschkabirk.bsky.social @kasieczka.bsky.social et al. we have trained a FM on 180M particle physics events from the LHC and shown the performance in generating synthetic data: arxiv.org/abs/2412.10504.
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collecte...
arxiv.org
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Reposted by Michael Krämer
milescranmer.bsky.social
Miles Cranmer @milescranmer.bsky.social · Jan 2
🚨 FINAL REMINDER 🚨: Multiple Postdoc and PhD positions in our AI + Physics cluster in DAMTP, Cambridge!

Deadlines:
- Postdoc: Jan 5th (Sunday)
- PhD: Jan 7th (Tuesday)

More info below – Please share with researchers and students who might be interested in joining us!
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Reposted by Michael Krämer
inspirehep.net
INSPIRE HEP @inspirehep.net · Jan 2
INSPIRE and social media: Update your INSPIRE profile to add your accounts to your profile and keep in touch with us.

blog.inspirehep.net/2024/12/insp...
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mikraemer.bsky.social
Michael Krämer @mikraemer.bsky.social · Dec 31
Madrid gets ready for the New Year... ¡Feliz año nuevo!
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Reposted by Michael Krämer
kasieczka.bsky.social
Gregor Kasieczka @kasieczka.bsky.social · Dec 17
We prepared 180M jets from 2016 CMS data taking to be easily used for machine learning & demonstrate their use for pre-training.

Data at: www.fdr.uni-hamburg.de/record/16505
Paper at: arxiv.org/abs/2412.10504

w/ @ozamram.bsky.social, @joschkabirk.bsky.social, @mikraemer.bsky.social & others
Aspen Open Jets: a real-world ML-ready dataset for jet physics
This dataset contains approximately 180 M boosted jets, derived from open data collected by the CMS experiment at the Large Hadron Collider (LHC) in 2016 — specifically the JetHT datastream — and pres...
www.fdr.uni-hamburg.de
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