PhD Position in Machine Learning for Biotechnology, Graz University of Technology, Austria

Summary

Graz University of Technology is offering a highly motivating PhD position through the doc.funds project “BiotechPredict,” funded by the Austrian Science Fund. This opportunity is ideal for candidates interested in the intersection of machine learning and biotechnology, aiming to contribute to cutting-edge research on enzymes and process optimization.

PhD Position in Probabilistic Machine Learning for Biotechnology, Graz University of Technology, Austria

Designation

PhD Candidate in Probabilistic Machine Learning for Biotechnology

Table

Research Area

The successful candidate will focus on:

• Probabilistic Machine Learning
• Bayesian Techniques/Optimization
• Causal Modeling for Biotechnology
• Enzyme Development and Process Optimization

Location

Graz University of Technology, Graz, Austria

Eligibility/Qualification

• Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Bioinformatics, Mathematics, or related fields.
• Excellent coding skills (Python, Julia, C++, etc.).
• Strong English communication skills (verbal and written).
• Recommended: Strong background in machine learning/artificial intelligence and/or bioinformatics, experience in probabilistic machine learning, inference techniques, and high degree of self-responsibility.

Job Description

Responsibilities

• Research (90%)
• Conduct research in the areas of Probabilistic Machine Learning, Bayesian Techniques, and Enzyme Design.
• Publish findings in leading AI/ML/Biotechnology journals and conferences.
• Collaborate on projects related to Biotechnology and Enzyme Design.
• Supervise Bachelor and Master students.
• Administrative Duties (10%)
• Manage project activities and participate in project meetings.
• Teaching (Optional)
• Contribute to teaching activities related to machine learning topics.

How to Apply

Please submit the following documents via the online application portal:

1. Brief motivation letter (max 2 pages)
2. CV with a list of publications (if available)
3. Master thesis or other scientific writings
4. Transcript of records
5. Recommendation letters (max 3, if available)

Last Date for Apply

15 July 2026

Seize this opportunity to join an interdisciplinary team dedicated to advancing the field of Biotechnology using state-of-the-art AI techniques.

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