Dear members of the HNR community,
via Prof. Dr. Matteo Valleriani (more info at https://www.jobs.tu-berlin.de/stellenausschreibungen/168158?language=en):
Research Assistant – salary grade 13 TV-L Berliner Hochschulen – For qualification
part-time employment may be possible
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) at Technische Universität Berlin (Prof. Dr. Klaus-Robert Müller) is seeking a Research Associate in Machine Learning for an Agility subproject. The agility project will be carried out in close cooperation with the project “The Sphere. Knowledge System Evolution and the Shared Scientific Identity of Europe” (https://sphaera.mpiwg-berlin.mpg.de) by Prof. Dr. Matteo Valleriani at the Max Planck Institute for the History of Science in Berlin.
Valleriani’s group is developing algorithms to study knowledge systems in the history of science. Building on a dataset extracted from astronomical tracts of the early modern period (ca. 1450-1650), the overall goal of the project is to identify mechanisms of knowledge evolution and to quantify these processes. Data refer to texts, images, and numerical computational tables.
Tasks
Independent and responsible research in the area of machine learning. The goal is to quantitatively determine semantic relations between texts.
The tasks involved are:
- Data extraction from over 110,000 pages of the Sphaera corpus
- Building efficient image segmentation pipelines and fine-tuning OCR approaches to adapt to different early modern print styles and languages
- Improve speech recognition for under-represented languages by transferring modern language technology, e.g., Large Language Models
- Developing and analyzing approaches for extracting historical insights from the results
- Communicating results through presentations
- Assist in the maintenance and enhancement of the Sphaera database (semantic technologies)
Required is the ability to interact with a team of historians and other ML-experts.
Requirements
- Successfully completed academic university degree (Master, Diplom or equivalent) in Mathematics, Physics, Computer Science, Data Science, Digital Humanities
- Demonstrated experience in machine learning and data science with a strong understanding of algorithms, statistics, and mathematical concepts
- Excellent programming skills in Python, and solid knowledge of common machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn
- Solid knowledge of SQL and SPARQL for efficient data extraction, manipulation, and analysis
- Good knowledge of knowledge graph data structure is desirable (e.g. RDF-data structure)
- Good understanding of network analysis
- Experience with version control systems (e.g., Git)
- Experience with Docker containers is a plus
- Strong communication skills in English and the ability to explain complex topics to a broad audience from diverse backgrounds (i.e., both historians and computer scientists)
- Basic knowledge of HTML and JavaScript is desirable
- Familiarity with SOTA machine learning models and approaches
- Familiarity with Explainable Artificial Intelligence (XAI)
- Excellent knowledge of English; good written and spoken German language skills desired, or willingness to learn
How to apply
Please send your written application, quoting the reference number, with the usual application documents to Technische Universität Berlin – Die Präsidentin – Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, Prof. Dr. Müller, MAR 4-1, Marchstr. 23, 10587 Berlin or by e-mail (one PDF-file, max. 5 MB) to: jobs@bifold.berlin.
Application documents sent by post will not be returned. Please submit copies only.
By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guarantee for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the Technische Universität staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/ or quick access 214041.
To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The Technische Universität Berlin values the diversity of its members and is committed to the goals of equal opportunities.
