BARC

BARC BARC is a leading center for fundamental algorithmic research, headed by VILLUM Investigator Mikkel Thorup.

Want to join the ambitious, creative, collaborative, and fun environment at BARC (https://barc.ku.dk)? Our annual call f...
26/11/2025

Want to join the ambitious, creative, collaborative, and fun environment at BARC (https://barc.ku.dk)? Our annual call for PhD or integrated MSc + PhD fellowship in Algorithms has been posted! Apply by January 10. https://employment.ku.dk/phd/?show=153212

The Department of Computer Science at the University of Copenhagen (DIKU) invites applications for PhD or integrated MSc + PhD fellowship in Algorithms.

Interested in doing a PhD in theoretical computer science at University of Copenhagen? Our annual call is up, applicatio...
05/12/2022

Interested in doing a PhD in theoretical computer science at University of Copenhagen? Our annual call is up, application deadline January 10. Join an ambitious, fun and creative environment in one of the world's most liveable cities!

 PhD positions in Theoretical Computer Science and/or Combinatorial Optimization Department of Computer Science Faculty of SCIENCE University of Copenhagen

10 papers authored or co-authored by members of BARC have been accepted to next year's SOSA and SODA conferences. Check ...
08/11/2022

10 papers authored or co-authored by members of BARC have been accepted to next year's SOSA and SODA conferences. Check out all the papers here:

Basic Algorithm Reasear Copenhagen is represented at next year’s SODA and SOSA conferences to be held in Florence, Italy, January 22-25, 2023. In total 10 papers are accepted in relation to BARC members.

PhD position in (algorithmic aspects of) differential privacy at University of Copenhagen, affiliated with BARC and the ...
19/10/2022

PhD position in (algorithmic aspects of) differential privacy at University of Copenhagen, affiliated with BARC and the Providentia project. Application deadline 2022-10-31.

 PhD fellowship in Computer Science PhD Project in Differential Privacy Computer Science Department Faculty of SCIENCE University of Copenhagen   We i

The BARC team is represented yet again at the prestigious STOC conference! We are happy to announce 4 accepted STOC pape...
21/02/2022

The BARC team is represented yet again at the prestigious STOC conference! We are happy to announce 4 accepted STOC papers in relation to BARC members this year. The BARC authors are Nutan Limaye, Thore Husfeldt, Mikkel Thorup, Ioana Bercea and Jakub Tetek.

The BARC team is represented yet again at the STOC conference.

BARC is presenting 5 exciting papers at FOCS21 this week! Check out which on our website.
07/02/2022

BARC is presenting 5 exciting papers at FOCS21 this week! Check out which on our website.

FOCS 2021, the IEEE Symposium on Foundations of Computer Science, is taking place this week as a virtual conference and again this year BARC is well represented.

Best Student paper at SODA22! 🏆BARC is always proud to present papers at the ACM-SIAM Symposium on Discrete Algorithms b...
10/01/2022

Best Student paper at SODA22! 🏆
BARC is always proud to present papers at the ACM-SIAM Symposium on Discrete Algorithms but this year we are especially excited to have two of our talented young PhD students Lorenzo Beretta and Jakub Tětek present their paper "Better Sum Estimation via Weighted Sampling", which was awarded Best Student Paper for SODA 22.

At SODA this year BARC is represented with two papers, one of which has been awarded Best Student Paper written by two of our PhD students Lorenzo Beretta and Jakub Tětek.

Training neural networks can drain the world of all power!A new paper proves that training neural networks is even harde...
08/12/2021

Training neural networks can drain the world of all power!
A new paper proves that training neural networks is even harder. All the world's energy can be used to train a single network of modest size without reaching the optimum. This calls for restricting the training process and clever ways to replace the use of neural networks.
Read the full article at BARC.KU.DK

A new paper proves that training neural networks is even harder. All the world's energy can be used to train a single network of modest size without reaching the optimum. This calls for restricting the training process and clever ways to replace the use of neural networks.

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