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We are seeking a postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data
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-supervised deep learning models that directly incorporate electromagnetic physics. We are looking for a motivated postdoc to lead cutting-edge research that combines methodological development with
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intelligence, starting 1 April 2027. You will design new sequence-to-expression deep learning models that predict gene expression from DNA sequence in plants, using new experimental datasets generated in the
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, Denmark, invites applications for a two-year postdoctoral position in plant regulatory genomics and artificial intelligence, starting 1 April 2027. You will design new sequence-to-expression deep learning
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algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems. The project focuses on the intersection of deep reinforcement learning
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learning. The required qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation
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Track Record: At least two first-author research papers at flagship venues of core machine learning research (e.g., NeurIPS, ICML, ICLR, AISTATS). Theoretical Rigor: A deep understanding of
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protein engineering, computationally designing antibodies, nanobodies, and binding proteins with condition-dependent activity. You will develop deep learning and generative models for binder design
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) to collect, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning
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computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply these methods across different crops to identify conserved