The ODSL provides a new pillar for novel analysis methods, algorithms and computational tools to fully exploit high-dimensional, complex data sets. The ODSL specialises in advanced techniques for pattern recognition in noisy data and for the identification and extraction of weak signals. Modern techniques in machine learning will be employed as well as augmented reality and visualisation techniques in the extraction of scientific results.
The Dark Matter Data Center (DMDC) is integrated into ODSL and serves the community fostering a culture of open data and methods.
We are looking for students interested in working on cutting-edge Data Science problems in various physics disciplines, e.g. particle physics, astrophysics or astrochemistry. This is an unpaid internship with an official certificate in the end. Working hours to be discussed.
Contact: Prof. Dr. Lukas Heinrich l.heinrich(at)tum.de
We are opening the second call for proposals from Origins Cluster scientists to collaborate with the ODSL team on data analysis projects. Our team has a wide range of expertise in applied statistics and can offer dedicated support to help you make the most of your data. We are looking for scientific projects with flexible durations, anything from a few weeks to many months.
Our core team currently consists of the two postdocs: Francesca Capel and Jakob Knollmüller and a PhD student. We are also joined by the four ODSL fellows, Johannes Buchner, Philipp Eller, Nahuel Ferreiro and Oliver Schulz. Together we have experience in a variety of data analysis topics including Bayesian analysis, Monte Carlo methods, hierarchical modelling, machine learning, likelihood-free inference and variational inference to give some examples.
In return, we expect acknowledgement or authorship on resulting publications, depending on the level of involvement. We want to make it clear that we are not offering help in setting up computing environments or basic software, nor are we a high-performance computing facility.
Proposals are welcome from all Origins member scientists but must be endorsed by an Origins PI. Proposals should include
If you have any questions regarding the proposals, then please contact us at odsl-team(at)origins-cluster.de. We are happy to discuss and help you to formulate your request. A selection committee made up of the ODSL core team, fellows, and Origins scientists from different disciplines will evaluate the proposals promptly after the submission deadline.
The deadline for this call is October 31st 2021.
Please send your proposal in English as a single pdf file to odsl-team(at)origins-cluster.de.
We are organising our next set of Block courses from September 6th - 16th 2021 under the title Practical Inference for Researchers in the Physical Sciences.
This session consists of two one-week courses:
1. Monte Carlo inference methods
Introduction to Bayesian inference with physical models. Parameter uncertainties, degeneracies and knowledge updates. Model comparison and criticism. Modern Monte Carlo algorithms for Bayesian inference in practice and probabilistic computation packages: Importance Sampling, Markov Chain Monte Carlo, Nested Sampling.
2. Bayesian workflow
Bayesian thinking, going from a science question to a generative statistical model, defining sensible priors, verification through simulations, diagnosing problems in models and computation, robust decision making, experiment design.
The courses will be held online and organised by Johannes Buchner and Francesca Capel. We plan to offer credits to both TUM and LMU students.
For more information and to register please visit: https://indico.ph.tum.de/event/6875/
Visit our projects page for an overview of our ongoing research projects.
We run a journal club to discuss data science topics every Friday at 2 pm. To join our mailing list and receive notifications, please send an empty email to odsl-subscribe(at)lists.lrz.de or visit this website: https://lists.lrz.de/mailman/listinfo/odsl.
If you have ideas for topics to discuss, feel free to propose them in the following google doc: http://bit.ly/odsljc20.
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