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PhD scholarship in Uncertainty Quantification for Deep Learning - DTU Compute

Do you want to do research on cutting edge machine learning methods?

If you are establishing a career as a researcher in machine learning, and you are motivated to work with the latest methods for quantifying uncertainty in neural networks, we can offer you the best possible foundation.

During the PhD program, you are expected to:

• Develop novel methods for uncertainty quantification in deep learning.
• Work with state-of-the-art nerual network architectures applied to molecular data.
• Publish scientific papers and present research results in top machine learning conferences such as NeurIPS, ICML, UAI, and AISTATS.

PhD Scholarships (2) in Cyber-Security and Cyber-Deception – DTU Compute

The two positions are part of the project Loki: Situational aware collaborative bio-inspired cyber-deception.

This project, inspired by Norse mythology, with Loki being a shape-shifter god and a master of trickery, aims at redefining and evolving the emerging field of cyber-deception. Here, we attempt to deceive attackers by creating fake vulnerable systems that are aware of their surroundings and are constantly shifting.

The project takes inspiration from nature (e.g., from the mimicry phenomenon) to synthesize sophisticated deception.

PhD scholarship in Cryptography – DTU Compute

We are looking for a bright, ambitious, and motivated PhD student to join the cryptography group in the Cybersecurity Engineering Section.

The goal of the PhD project is to improve the state of threshold post-quantum cryptography.

Your main task will be to design new threshold cryptographic algorithms with post-quantum security.

Postdoctoral positions in Sustainability, Genomics, Microbiology, and Bioinformatics/Data Science - DTU Biosustain

DTU Biosustain is actively seeking candidates for postdoctoral roles in fields such as sustainability, genomics, microbiology, and bioinformatics/data science.

We welcome applications from researchers in these or related fields, especially those focusing on large-scale data integration using advanced technologies (e.g., long-read, single-cell, microscopy, metabolic) for sustainable food and agricultural production.