Experimental Astroparticle Physics with Deep Learning

The Astroparticle physics group at TU Dortmund University is engaged in many of the world’s leading astroparticle physics experiments and has pioneered the use of modern deep learning techniques to advance fundamental physics research. We are now expanding our activities and invite applications for several PhD and PostDoc positions at the intersection of high-energy astroparticle physics, deep learning, and differentiable programming.

Successful candidates will contribute to projects such as the ERC-funded NuRadioOpt initiative, which includes the development of a deep-learning-based trigger to dramatically increase the detection rate of ultra-high-energy neutrinos, the end-to-end optimization of the detector design using differentiable programming for next-generation experiments such as IceCube-Gen2, and the creation of a new data acquisition system that leverages state-of-the-art, low-power, high-speed ADCs and FPGA-based real-time inference. Additional opportunities include participation in the Radio Neutrino Observatory in Greenland (RNO-G), the IceCube Neutrino Observatory at the South Pole, and the Pierre Auger Cosmic Ray Observatory in Argentina, among others, to study the particle physics of air showers. Candidates will also have the opportunity to contribute to the development of the open-source simulation frameworks NuRadio and CORSIKA 8, providing the theoretical foundation for the experimental work.

By joining us, you will become part of an active astroparticle physics group at TU Dortmund and collaborate closely with the local LHCb group on advancing CORSIKA 8 and exploring the particle physics of air showers. Your work will be embedded in international collaborations such as RNO-G, IceCube, Pierre Auger, and MODE, and you will profit from a vibrant local research network through the Collaborative Research Center SFB 1491 (connecting Dortmund, Bochum, Wuppertal, and Duisburg-Essen) and the LAMARR Institute for Machine Learning and Artificial Intelligence, fostering collaboration across physics and AI.

The positions have full benefits and include support for travel and research expenses. PhD positions are three years, and PostDoc positions are initially two years.

Requirements:

• We are looking for a qualified and highly motivated candidate with a master’s degree or equivalent in physics or a related field with excellent results. For PostDocs, a PhD in Astroparticle physics or equivalent is required.
• Excellent communication and writing skills in English, as well as the ability to work both independently and collaboratively, are required.
• Applications may be submitted by candidates who have not fully completed the Master of Science degree or PhD – but please state your earliest starting date.

Additional qualifications:

• Good Python programming and data analysis skills are a benefit, but there will be opportunities to improve those skills.
• Experience in radio detection of cosmic rays or neutrinos and/or deep learning is a merit.
• For the hardware projects, experience with FPGA programming is a benefit.

The application should include a statement of research interests and relevant experience, a CV, and a transcript of grades (please indicate grading system if not evident). Please provide the names and contact information for 2-3 people who can act as references. The positions should be ideally filled as soon as possible. Please indicate your earliest starting date. Please compile your application in a single PDF file and send it to app.physik@tu-dortmund.de with the subject line “application PhD” or “application PostDoc”. Applications will be reviewed as they arrive. The application remains open until all positions are filled. All applications received until October 19 will receive full consideration.

For further information, please contact: Prof. Dr. Christian Glaser (christian.glaser@tu-dortmund.de).

TU Dortmund University is an equal opportunity employer and welcomes applications from women. Handicapped applicants will be treated preferentially if equally qualified.