InstitutionCluster of Excellence "Understanding Written Artefacts"

Salary levelEGR. 13 TV-L

Start date01.01.2026, fixed until 30.06.2029 (This is a fixed-term contract in accordance with Section 2 of the academic fixed-term labor contract act [Wissenschaftszeitvertragsgesetz, WissZeitVG]).

Application deadline02.10.2025

Scope of workfull-time position suitable for part-time

The Cluster of Excellence ‘Understanding Written Artefacts’ (UWA) is looking for a doctoral researcher in the field of Computer Science for the Visual Manuscript Analysis Lab (VMA). UWA offers an exciting opportunity to research handwritten artefacts across materials, periods and regions within a collaborative team of doctoral, postdoctoral and senior/professorial researchers. You will be part of an international team of researchers from across over forty disciplines in the Humanities, the Natural Sciences and Computer Science. The Centre for the Study of Manuscript Cultures is by now the largest research centre of its kind worldwide and offers a uniquely dynamic research environment.

Your responsibilities

Duties include academic services in the project named above. Research associates may also pursue independent research and further academic qualifications. They may also pursue doctoral studies outside of working duties.

Your core responsibility is to pursue your research project, involving to design, implement, and evaluate machine learning approaches relevant to the project’s objectives. You will contribute to the research activities of the VMA lab, including meetings, seminars, and collaborative projects within the cluster UWA. Eventually, you will be a part of the cluster’s Graduate School and participate actively in its colloquia and collaborative research networks.

Your profile

A university degree in a relevant field.

Fluent English, written and spoken.

A university degree (Master or equivalent) in computer science, artificial intelligence, data science, or a closely related discipline is preferred. You have strong theoretical and practical knowledge of machine learning, as well as foundational understanding of computer vision and vision–language learning, including familiarity with relevant software packages and libraries. A demonstrable ability to implement state-of-the-art machine learning methods, evidenced by, for example, publicly accessible code repositories, is desirable.

Further preferred qualifications and experience include:

• research or project experience in computer vision approaches
• familiarity with natural language processing
• practical experience with vision–language models, such as CLIP and InstructBLIP

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

The successful candidate can benefit from a range of tailored support measures at the Cluster, which include,

• enrolment in the clusters Graduate School with an interdisciplinary structured PhD programme
• additional funding for a range of research activities, including fieldwork
• doctoral mentoring programmes
• workshops to support your academic skills as well as your career planning

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.

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