Postdoc in Multimodal Generative AI
Postdoc in Multimodal Generative AI
KTH - Royal Institute of Technology
Kista, Sweden
Job description
Graph generative models are recently gaining significant interest in current application domains. They are commonly used to model social networks, knowledge graphs, and protein-protein interaction networks. The research to be conducted during this project will capitalize on current results on generative models for proteins and graphs. We will investigate the challenges of multi modality in the context of defining architectures for graph generation under the proper prompt. We expect our designed architectures to be useful in different areas including molecule design, medical data generation, bio related data.
Qualifications
Requirements
- A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made;
- Candidates must have a recent PhD degree in either Computer Science, Mathematics, Chemistry, Biology, or Physics;
- Analytical abilities, creative thinking, proficiency in deep learning programming (using Python and PyTorch);
- A strong mathematical background encompassing Probability, Statistics, and Linear Algebra.
Preferred qualifications
- A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline;
- In addition to technical expertise, we value a strong machine and deep learning skills, particularly in architecture design and optimization;
- A solid understanding of advanced architectures like Transformers or Graph Neural Networks is a plus;
- A background in biological application domains to bridge the gap between machine learning and real-world biological problems;
- A demonstrably impactful publication record is a key factor in evaluating potential candidates.
Great emphasis will be placed on personal skills.
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