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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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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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sciences Strong background in deep learning, with experience in probabilistic models (e.g., Variational Autoencoders, Bayesian approaches) Proficient Python programming for machine learning and scientific
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-facing messaging aligned with EQA’s objectives. Cross-team coordination and operational leadership Acting as a bridge between scientific content, project operations, and consortium dynamics. Supporting
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behaviour. Your primary responsibilities will include: Implementing technologies to track human behaviour in laboratory-based conversation studies Designing and conducting research studies with multiple
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entities, such as robots, vehicles, or sensors, forms internal representations of space, time, and motion when interacting in complex non-stationary environments. The objective is to study and develop models
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border. CIE is a new initiative striving for high quality and great impact of its research, innovation, and education. Central to achieving this objective is access to state-of-the-art facilities and
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combining CRISPR-based endogenous protein tagging, advanced quantitative imaging, and biochemical approaches, this project seeks to uncover novel principles of replisome plasticity. A key long-term objective
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endogenous protein tagging, advanced quantitative imaging, and biochemical approaches, this project seeks to uncover novel principles of replisome plasticity. A key long-term objective is to identify cancer
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out within the Centre’s strategic research areas and objectives, contributing to ongoing projects within one or more of the thematic areas (listed below), with a focus on the development of high-quality