Are you interested in exploring probabilistic machine learning to tackle important challenges in bioscience? Aalborg University, Denmark, invites you to join a collaborative project where probabilistic machine learning meets bioscience to address critical environmental issues and explore the complexities of microbial communities. At the Technical Faculty of IT and Design, Department of Computer Science, a position as Postdoc is open for appointment from 15. March, 2025 or soon hereafter. The postdoc position is initially for one year, with the possibility of an extension for up to 2 years based on mutual agreement.

JOBBESKRIVELSE

We are seeking a highly motivated post-doctoral researcher to develop scalable probabilistic machine learning models and algorithms for representation learning, uncertainty quantification, and model interpretability over large, heterogeneous datasets. These include sequenced microbial DNA fragments enriched with auxiliary environmental data, providing exciting opportunities for novel research.
This position is part of the VILLUM Synergy project:“Illuminating microbial dark matter through data science”. You will be part of an exciting cross-disciplinary project combining probabilistic machine learning and bioscience, with a focus on methods for improving our understanding of the microbial communities in our environments. These communities play a vital role in most processes in the biosphere and are essential for solving present and future environmental challenges.

What you’ll do

• Design and implement probabilistic machine learning techniques, focusing on generative and Bayesian models.
• Analyze large-scale environmental datasets to recover and explore microbial genomes.
• Collaborate with researchers in computer science and biosciences to produce high-impact research.
• Publish your findings in top-tier international conferences and journals within machine learning and computer science.

We’re looking for a candidate with:

• PhD in computer science, bioinformatics, data science, or a closely related field.
• Expertise in probabilistic machine learning, optionally generative and Bayesian models.
• A strong publication record in relevant fields.
• Solid programming skills and a collaborative mindset.
• Excellent communication skills in English(written and oral).

Experience in interdisciplinary research is a plus!
Why Aalborg University?
The Computer Science Department at Aalborg University takes a leading international position within data management and verification. It is a very young university(1974) but with a strong international profile in Mathematics, Computer Science& Engineering, also hosting the two most highly cited Computer Scientists of the country. According to the most recent US News and World Ranking, Aalborg University ranks 4th in the world within the field of Engineering. According to a 2018 MIT report, Aalborg University is considered to have the 4th best engineering program in the world.
Why Aalborg?
Aalborg is a lively city with numerous cultural attractions. The area features relatively low cost of living, clean air, beautiful forests and beaches, and very good transportation infrastructure. A recent report on European cities ranks Aalborg highest:http://ec.europa.eu/regionalpolicy/sources/docgener/studies/pdf/urban/survey2013en.pdf. For more information, seehttps://www.visitaalborg.com/ .

QUALIFICATION REQUIREMENTS

Appointment as Postdoc presupposes scientific qualifications at PhD–level or similar scientific qualifications. The research potential of each applicant will be emphasized in the overall assessment. Appointment as a Postdoc cannot exceed a period of four years in total at Aalborg University.

• Design and implement probabilistic machine learning techniques, focusing on generative and Bayesian models.
• Analyze large-scale environmental datasets to recover and explore microbial genomes.
• Collaborate with researchers in computer science and biosciences to produce high-impact research.
• Publish your findings in top-tier international conferences and journals within machine learning and computer science