The MIAO research group is active at both the University of Copenhagen and Lund University, and all members have offices in both Copenhagen and Lund. Sometimes we are announcing positions simultaneously in both Copenhagen and Lund. In such cases, it is a good idea to send in two copies of your application, one each to Copenhagen and Lund, in order to keep your options open. For formal reasons, you can only be hired for a position if you have applied specifically at the university where the position is (but sending in exact copies of the same application is fine, and you can also mention in your application that you are applying to both places).
Please feel free to drop me a line if you have any questions about any of the openings advertised (or not advertised) on this webpage. Note, however, that we cannot accept applications via e-mail — we can only hire for currently open positions as advertised in official announcements, and all applications must be made via the official recruitment systems (as per instructions in the announcements).
We currently have no openings in theoretical computer science at the Department of Computer Science at the University of Copenhagen, but expect to hire in TCS going forward. Please do not hesitate to contact me and/or Mikkel Thorup if you are interested in learning more about opportunities to join one of the most exciting and dynamic TCS environments in Europe!
We currently have no open faculty positions in algorithms or complexity theory at the Department of Computer Science at Lund University, but this webpage will be updated as we publish new openings.
We expect to be advertising postdoc openings again in the autumn of 2023, both in theoretical computer science and in combinatorial optimization, with an intended starting date in the autumn of 2024.
If you instead want to start collecting grants to list on your CV, another excellent opportunity is to apply for a postdoctoral fellowship within the EU Marie Skłodowska-Curie actions program to come and do a postdoc with me. The next call has the deadline September 13, 2023. I would particularly welcome applications from strong candidates who want to work in computational complexity theory, SAT solving, integer linear programming, constraint programming, or some mix of these areas.
Please feel free to contact me if you would wish to discuss any of the opportunities mentioned above.
All postdoc positions in my research group are fully funded, employed positions (including travel money) that come with an internationally competitive salary.
I am looking for a PhD student in theoretical computer science and/or combinatorial optimization at Lund University on a project aiming to go significantly beyond the state of the art in either combinatorial solving (as explained in this Simons Institute tutorial) and/or provably correct computation (as per this YouTube tutorial). The application deadline is June 1, 2023. Early applications are welcome, since this will help speed up the recruitment process, but all applications submitted before the deadline will be given equal consideration. The starting date is flexible, but should ideally be during the autumn of 2023.
We expect to have PhD openings again in the late autumn or winter of 2023 with an intended starting date in the autumn of 2024. The webpage will be updated as soon as we know more about this.
All our PhD positions are fully funded, employed positions (including travel money) that come with an internationally very competitive salary.
I would be interested in supervising students for thesis work on topics as briefly outlined below. Please note that the descriptions are in no way intended to be exhaustive. You can send me an e-mail to get more detailed information with an up-to-date list of more concrete proposals. (Note, however, that these projects are intended for students who are geographically close and can work on their thesis in the Copenhagen-Lund area. There is no dedicated funding available to support foreign students to come to Denmark or Sweden.)
These projects are intended to give students a feel for what research in computer science is like, while at the same time focusing on concrete problems of both theoretical and practical importance. Apart from the thesis work itself, the intention is that the results of successful thesis projects should also be exciting enough to be published as (parts of) papers in leading scientific conferences and/or journals in the field (within the framework of the research outlined here).
Formula Hardness and SAT Solving
Given a formula in propositional logic, is it possible to set its variables in such a way that the formula is satisfied? This simple looking problem has been on centre stage in theoretical computer science ever since the field got started some 40 years ago, and has been named as one of the Millennium Prize Problems comprising some of the major challenges for all of mathematics in the 21st century. Today, students of computer science worldwide learn in their introductory algorithms and complexity courses that this so-called SAT problem is what is known as NP-complete, and therefore is very, very hard in practice.
Interestingly, practioners take a somewhat different view. During the last 20 years, SAT has developed from a problem of mainly theoretical interest into a practical approach for solving a wide range of applied problems. Enormous progress in performance has led to satisfiability algorithms, so-called SAT solvers, becoming a standard tool for solving real-world problems with millions of variables in the context of, for example, hardware and software verification, electronic design automation, artificial intelligence, operations research, and bioinformatics. The theory of NP-completeness did not quite go away, however — for all these SAT solvers there are also known examples of tiny formulas with just a couple of hundred variables that make them fail miserably.
How can modern SAT solvers be so good in practice? How can one know for a particular formula whether it will be hard or easy? Can we extend SAT solvers with new methods of reasoning to make them potentially even more powerful than the best solvers today? These are the kind of questions we want to study in these Master's thesis projects, using a mix of theoretical research and practical experiments.
Solving 0-1 Integer Linear Programs
A 0-1 integer linear program (ILP) is a collection of linear constraints
a1 x1 + a2 x2 + … + an xn ≥ A
where all ai and A are integers and the variables xi can only take values 0 or 1. Such 0-1 ILPs can be used to model a vast range of problems in computer hardware construction and verification, software analysis, scheduling, logistics, disaster management, bioinformatics, artificial intelligence, et cetera.
Most of these problems are known to be computationally very, very challenging from a theoretical point of view — it is widely believed that it is impossible to design general-purpose algorithms that will always run fast and produce correct solutions — but in practice there are very efficient combinatorial optimization solvers that try to exploit the structure of concrete problems to find shortcuts, and use an array of sophisticated heuristics to do so. For most of these heuristics there is an almost complete lack of rigorous scientific understanding of when and why they work well or fail, however.
Another intriguing fact is that recent research on combining methods from Boolean satisfiability (SAT) solving, pseudo-Boolean solving, and mixed-integer linear programming (MIP) has shown that adding together solving techniques from these different areas can lead to major improvements in performance.
A project in this area could involve: