6 PhD Scholarships: Center for Basic Machine Learning Research in Life Science (MLLS) 912 views

The Center for Basic Machine Learning Research in Life Science (MLLS) gathers machine learning researchers from the Departments of Biology and Computer Science, University of Copenhagen (UCPH) and DTU Compute, the Technical University of Denmark (DTU). It is funded by the Novo Nordisk Foundation (NNF) through their Data Science Initiative. The Center is inviting applications for six PhD scholarships. The PhD scholarships are commencing 1 May 2021 or as soon as possible thereafter.

Project description

1. Informative identifiable geometric representations
2. Structured encoder-decoder models for biological data
3. Topology-aware models
4. Interpretable generative and predictive models
5. Representation learning for biological sequences
6. Predictive models for network data
7. Deep generative models for structured and heterogeneous data
8. Uncertainty in predictive models
9. Uncertainty and robustness in generative modeling

Requirements:

• Applicant must hold master’s degree should preferably be in computational science and engineering (CSE), computer science, statistics, pure/applied mathematics, physics, bioinformatics or equivalent academic qualifications.
• Preference will be given to candidates who are excited about basic research, have an interest in the mathematical foundations of machine learning and have experience with mathematical modelling and the programming and tools (e.g., PyTorch/TensorFlow) needed to apply these models to real data.
• Applicant must possess good command of the English language.

Applications:

Apply online and include the following:

• Cover Letter, stating which PhD project(s) he/she is applying for and detailing motivation and background for applying for the specific PhD project.
• CV
• Diploma and transcripts of records (BSc and MSc)
• Acceptance Letter for the relevant MSc Programme at SCIENCE, if any
• Other information for consideration, e.g. list of publications (if any)
• 1-3 reference letters (if any)
• Include a link to an example of a technical text candidate has written in English such as a report prepared for a course, your BSc or MSc thesis, a consulting project, or a scientific article that is in preparation or has been published with peer review.