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About the Role

digiS, the Berlin Research and Competence Center for Digitization, is seeking to fill a full-time position for a DFG-funded project focused on creating an annotated image corpus of public spaces in Berlin for AI model utilization. The role involves developing and evaluating AI-based pipelines, designing metadata schemas, analyzing data biases, and preparing datasets for machine learning.

Responsibilities

Development and evaluation of an AI-based pipeline for segmentation and identification of urban space elements in image corpora.
Conception of a metadata and annotation schema for capturing temporal, geographical, and thematic features, as well as essential provenance and licensing information.
Analysis of biases, representation gaps, and uncertainties in the data corpus and resulting models.
Conception and compilation of training, validation, and test datasets for machine learning methods.
Provision of the data corpus according to FAIR principles within existing scientific information infrastructures.
Conception and execution of knowledge transfer workshops for partner institution staff in collaboration with the digiS team.
Technical coordination of collaboration with project partners from the cultural heritage sector and technical coordination with the non-academic project position.
Participation in project planning, reporting, fund appropriation, and communication with funding bodies and cooperation partners.
Presentation and publication of research results, and active participation in project-related networking.

Requirements

Successfully completed academic degree in Computer Science, Mathematics, Digital Humanities, or a related discipline.
Good programming skills in at least one relevant programming language and proficient use of Git.
Secure application and needs-based adaptation of methods for data cleaning, aggregation, annotation, curation, and harmonization.
Proven experience with machine learning methods, particularly fine-tuning Foundation Models for labeling tasks.
Experience with computer vision applications, especially in filtering, feature extraction, object detection, and segmentation.
Knowledge of FAIR principles and common Open Science practices.
Very good German and English language skills, both written and spoken.
Knowledge of other relevant programming languages.
Experience in data quality management of AI pipelines throughout the entire data lifecycle – from data acquisition to model output evaluation.
Knowledge of mathematical optimization methods, especially for hyperparameter optimization, and statistical methods, particularly sampling theory.
Experience with image annotation tools, e.g., Label Studio.
Experience in research data management and knowledge of metadata formats common in the cultural heritage sector (e.g., LIDO, EAD, METS/MODS) and controlled vocabularies (e.g., GND, AAT).
Knowledge of legal and ethical frameworks for handling digital image collections and AI training data.
Experience in coordinating interdisciplinary and/or third-party funded projects.

Skills

Computer Science
Mathematics
Digital Humanities
Programming
Git
Data Cleaning
Data Aggregation
Data Annotation
Data Curation
Data Harmonization
Machine Learning
Foundation Models
Computer Vision
Filtering
Feature Extraction
Object Detection
Object Segmentation
FAIR Principles
Open Science
German
English
Data Quality Management
Mathematical Optimization
Hyperparameter Optimization
Statistical Methods
Sampling Theory
Image Annotation Tools
Research Data Management
Metadata Formats
LIDO
EAD
METS/MODS
Controlled Vocabularies
GND
AAT
Legal Frameworks
Ethical Frameworks
Project Coordination

Location

Berlin

Work Type

Full-time
Part-time

Experience Level

Entry-level
Mid-level

Education Level

Academic degree in Computer Science, Mathematics, Digital Humanities, or a related discipline

Salary/Compensations

Entgeltgruppe 13 TV-L

Benefits

Active onboarding process
Family-friendly work environment with flexible working and meeting times
Varied, future-oriented, and responsible area of responsibility
Professional development opportunities and support for professional growth
Supplementary pension plan (VBL)
30 days annual leave
Flexible working hours (flexitime)
Annual special payment
Capital city allowance of up to €150 per month or BVG-Jobticket + difference
Subsidized use of canteens and sports programs of FU Berlin

About the Company

The Zuse Institute Berlin is an interdisciplinary research institute of the state of Berlin that combines Artificial Intelligence and Scientific Computing with application-oriented mathematics and High-Performance Computing.
Together with partners from science, technology, and industry, we develop computer-aided methods for solving socially relevant problems and provide state-of-the-art high-performance computing, AI, and data management infrastructures.

Equal Opportunity

The Zuse Institute Berlin is committed to diversity, equal opportunities, and respectful interaction.
To increase the proportion of women in areas where they are underrepresented, applications from women are expressly welcome.
Regardless of this, we welcome applications from all qualified individuals regardless of age, disability, ethnic or social origin, religion or belief, or sexual orientation or gender identity.