InstitutionFaculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Signal Processing (SP) Research Group

Salary levelEGR. 13 TV-L

Start date01.06.2026, fixed until 30.09.2027 (This is a fixed-term contract in accordance with Section 2 of the academic fixed-term labor contract act [Wissenschaftszeitvertragsgesetz, WissZeitVG]).

Application deadline15.03.2026

Scope of workfull-time position suitable for part-time

Your responsibilities

Duties include academic services in the project named above. Research associates may also pursue further academic qualifications outside of their work responsibilities.

The project funded by the Federal Ministry of Research, Technology and Space (BMFTR) aims to develop compressed diffusion models for audio and image signal processing. Diffusion models refer to a specific class of generative AI models that have shown excellent performance as data-driven priors for inverse problems in speech processing. However, current approaches remain computationally expensive as they require extensive neural network evaluations. The goal of this project is to significantly reduce training and inference costs by designing compressed data representations, efficient model architectures, and robust generative algorithms.

For this position, the tasks include developing lightweight diffusion models for audio and imaging applications, creating and evaluating compressed data representations and neural network architectures, and analyzing model robustness under distribution shifts and challenging conditions. Further responsibilities involve conducting independent research, writing scientific publications, presenting results at international conferences and workshops, and collaborating closely with our project partners at University of Hamburg (UHH), DESY, and FAU. The research results can contribute directly to the candidate’s doctoral dissertation.

The advertised position is primarily aimed at postdoctoral researchers. However, outstanding candidates who have completed a Master’s degree and are interested in pursuing a doctorate are also encouraged to apply.

Your profile

A university degree in a relevant field.

Interested candidates must have obtained a master's degree prior to the commencement of employment. Examples of a university degree in a relevant field include Computer Science, Data Science, and Electrical Engineering. Excellent knowledge in statistical signal processing and machine learning is required, along with good programming skills in Python, experience with modern machine learning libraries, and experience with speech processing and audio processing. Fluent English, both spoken and written, and good communication skills are mandatory. Knowledge of German is helpful; for non-native German speakers, we expect the willingness to learn German.

We offer

• Reliable remuneration based on wage agreements
• Continuing education opportunities
• University pensions
• Attractive location
• Flexible working hours
• Work-life balance opportunities
• Health management, EGYM Wellpass
• Educational leave
• 30 days of vacation per annum

Universität Hamburg—University of Excellence is one of the strongest research educational institutions in Germany. Our work in research, teaching, educational and knowledge exchange activities is fostering the next generation of responsible global citizens ready to tackle the global challenges facing us. Our guiding principle "Innovating and Cooperating for a Sustainable Future in a digital age” drives collaboration with academic and nonacademic partner institutions in the Hamburg Metropolitan Region and around the world. We would like to invite you to be part of our community to work with us in creating sustainable and digital change for a dynamic and pluralist society.

The University of Hamburg is committed to equity. Diversity enriches our university life, whether in our studies, research, teaching, education, or workplace. We therefore welcome all applications, regardless of gender, gender identity, sexual orientation, ethnic or social background, age, religion or belief, disability, or chronic illness.

The University of Hamburg strives to increase the number of women in academia, and encourages qualified female academics to apply.

Severely disabled and disabled applicants with the same status will receive preference over equally qualified non-disabled applicants.