PhD Stipend in Probabilistic Machine Learning: Aalborg University, Denmark, is offering a PhD stipend in the field of probabilistic machine learning as part of an exciting collaborative project focused on microbial bioscience. This role provides a unique opportunity to develop innovative probabilistic ML methods applied to real-world scientific challenges, particularly in the biosciences field.
Designation
PhD Student
Research Area
Probabilistic Machine Learning and Microbial Bioscience
Location
Aalborg University, Aalborg, Denmark
Eligibility/Qualification
- A Master’s degree in Computer Science, Artificial Intelligence, Statistics, Data Science, or a closely related field.
- Basic understanding of (probabilistic) machine learning.
- Solid programming skills.
- Collaborative mindset and strong communication skills in English (both written and oral).
- Experience in interdisciplinary research is advantageous.
Job Description
As a PhD student, you will:
- Conduct original research in probabilistic machine learning, focusing on generative and Bayesian models.
- Analyze large-scale environmental datasets to explore microbial genomes.
- Collaborate with researchers from both computer science and biosciences to create impactful research.
- Publish findings in reputable international conferences and journals within machine learning and computer science.
How to Apply
Interested candidates must submit the following documents via Aalborg University’s recruitment system:
- A cover letter stating reasons for applying and qualifications related to the position.
- Curriculum Vitae (CV).
- Bachelor’s and Master’s degree diplomas.
- Any other relevant documents.
For additional information and to access the application portal, visit the job advertisement on Aalborg University’s website.
Last Date to Apply
June 9, 2025
This opportunity is part of the VILLUM Synergy project “Illuminating microbial dark matter through data science.” If you are passionate about addressing scientific challenges through machine learning, consider applying for this unique PhD position.
