PhD Position in NLP - Multimodal / multi-agent NLP evaluation

Job Information; Deutschland; Vor Ort; EUR 40.000 - 55.000; Vollzeit

Zusammenfassung

A leading university in Germany is seeking a motivated PhD candidate to join their research team in Natural Language Processing. The role involves developing evaluation metrics for generative AI, collaborating with peers, and publishing research findings. Candidates should possess a strong academic background and proficiency in Python, with a focus on interdisciplinary research. This is a full-time position with a starting date in January 2025.

Qualifikationen

• Outstanding Master’s degree in relevant fields required.
• Strong background in NLP, machine learning, or computer vision.

Aufgaben

• Conduct research and teaching at the NLLG Lab.
• Collaborate with other researchers and publish results.

Kenntnisse

Natural Language Processing

Machine Learning

Computer Vision

Python

Analytical Skills

Ausbildung

Master’s degree in Natural Language Processing

Master’s degree in Computational Linguistics

Master’s degree in Computer Science

Master’s degree in Artificial Intelligence

Tools

PyTorch

TensorFlow

Jobbeschreibung

Organisation/Company University of Technology Nuremberg Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 2 Nov 2025 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Jan 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Multimodal / Multi-Agent Evaluation of Generative AI

We are seeking a highly motivated and talented individual to join our dynamic and international research team and contribute to cutting-edge research in the field of multimodal / multi-agent NLP evaluation. The focus will be on developing robust, efficient, and high-quality evaluation metrics for text generation/generative AI. These metrics may extend existing methods like BERTScore, BARTScore, or GEMBA to multimodal settings, such as text-to-image generation, or incorporate multi-agent approaches like multi-agent debate for evaluation.

Your Main Tasks:

• Research and teaching at the Department of Engineering of UTN in the Natural Language Learning & Generation (NLLG) Lab (https://nl2g.github.io/ )
• Collaboration with other researchers
• Publication of your research results at top-quality conferences and journals

Your Profile:

• An outstanding Master’s degree in Natural Language Processing, Computational Linguistics, Computer Science, Computer Vision, Artificial Intelligence, or a related field
• A strong background and genuine interest in NLP, machine learning, or computer vision and interdisciplinary research
• Proficiency in Python and familiarity with machine learning frameworks like PyTorch or TensorFlow and handling of LLMs
• Strong mathematical, problem-solving, and analytical skills
• Effective communication and presentation abilities in English

Interested?
To apply for admission to doctoral research, please send your application until 02.11.2024 (Code: ENG-NLLG-24-06) to [e-mail] . Your application should include:

• A personal statement explaining why you want to pursue a doctorate in this area at UTN (limited to one page)
• Certificates of your university degrees (M.Sc. and B.Sc.) or equivalent qualification
• Transcripts of records, diploma supplements, or an overview of courses from your degrees (M.Sc. and B.Sc.)
• Your M.Sc. thesis
• (Optional) A link to your GitHub projects and any prior publications
• All application documents should be submitted together in ONE SINGLE PDF

If you are shortlisted, you will be invited for an interview, a research presentation, and a coding or analytical exercise.

• research experience is highly advantageous

Languages ENGLISH Level Excellent

Additional Information

Work Location(s)

Number of offers available 1 Company/Institute Natural Language Learning & Generation (NLLG) Country Germany Geofield