6 deep-learning Postdoctoral research jobs at Technical University of Denmark in Denmark
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Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
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recombinant minibinders for migraine-associated receptors. The project aims to advance deep learning–based molecular generation and structure-guided design for therapeutic innovation. We seek a highly motivated
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design platforms for rapid deisgn and optimisation of novel targeting modules. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein
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for screening purposes and cell-based therapies. We will develop methods for modelling missing not at random (MNAR) observations and quantifying uncertainty using Bayesian methods and deep learning architectures
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serving (Ray/VLLM), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression Extensive knowledge of agentic AI systems research, engineering and
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of operating underground with minimal human intervention. By joining this project, you will strengthen your scientific profile while gaining deep hands-on experience in mobile manipulation, contact-rich robotics