PhD Position in Interpretability of Protein Language Models
Explore explainable AI for protein language models in a fully funded PhD position at Leipzig University’s LION Lab.
Best for:Best for master’s graduates in computational biology, bioinformatics, or AI with a passion for explainable AI and protein sequence analysis.
Overview
This fully funded PhD position at Leipzig University’s LION Lab focuses on advancing interpretability techniques for protein language models. Under the supervision of Leonie Weissweiler and Clara Schoeder, the successful candidate will conduct cutting-edge research at the intersection of computational biology and natural language processing. The role involves developing methods to make protein language models more transparent and interpretable, bridging gaps between AI and biological sciences. Ideal for master’s graduates with backgrounds in computational linguistics, bioinformatics, computer science, or related fields, this position offers a unique opportunity to contribute to both AI and biomedical research.
What you'll do
- Develop novel interpretability techniques for protein language models to enhance transparency and biological relevance.
- Conduct research at the intersection of AI and computational biology, focusing on explainable methods for protein sequence analysis.
- Collaborate with supervisors to design and implement experiments to evaluate interpretability frameworks.
- Publish findings in top-tier conferences and journals within the fields of AI and computational biology.
- Present research results at international conferences and workshops.
- Contribute to the development of open-source tools or frameworks for interpretability in protein language models.
Requirements
Degree required: Master’s degree in computational linguistics, bioinformatics, computer science, data science, or a related discipline
Degree offered: PhD
Language: English; proficiency sufficient for academic research and publication
Instruction: English
- Strong background in computational biology, bioinformatics, or machine learning
- Proficiency in programming languages such as Python
- Experience with protein sequence analysis or natural language processing
- Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
- Ability to work independently and collaboratively in a research environment
- No specific nationality or residency restrictions mentioned
- No age or prior experience caps
Funding & terms
How to apply
Deadline: 2026-10-15
Applicants should submit their CV, a cover letter outlining their research interests and qualifications, and a research proposal or statement of purpose through the provided application portal. Additional documents may be requested based on the application review process.
- CV
- Cover letter
- Research proposal or statement of purpose
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