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Financing yes Type of Position Full PhD Working Language English Required Degree Master Areas of study Physics, Experimental Physics Description Description For our BMBF-funded research project
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applications from prospective PhD students who are interested in performing research in Mathematical and Theoretical High Energy Physics. The PhD work will be carried out in the group of Prof. Alba Grassi
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 22 hours ago
Job Code: 16/2025 Job Offer from July 24, 2025 The Max Planck Institute for Extraterrestrial Physics (MPE) in Garching is a world leader in the field of ground-based and space-based experimental
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University/NL. The successful candidates commit to actively participate in networking including regular research visits to the partner laboratories. Requirements: university degree in chemistry or physics and
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job description and the link for submitting the application material under reference number 25063. If you have questions on the submission process or have questions on the position please contact Prof
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Industrial Doctoral Landscape Award (IDLA) in Gas Turbine Heat Management. Supervisors: Prof Peter Ireland Future aircraft engines will maximize fuel efficiency by including new, fluid flow and cooling systems
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for Theoretical Physics at the University Tübingen, Germany, in the group of Prof. Daniel Braun. Depending on a DFG funding decision, an upgrade to 75% of an E13 TV-L position might be possible after the first year
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academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
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Closing Date: 15th September 2025 [23:59 GMT] Supervisor: Prof M. Sumetsky Prospective Start Date: 1 January 2026 Applications are invited for a Postgraduate studentship, supported by Aston
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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly