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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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. Your profile Applicants should hold a PhD in computer science, bioinformatics, or a related field, or expect to complete it before the start date. They should have research experience in deep learning
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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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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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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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-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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Biotech Research and Innovation Centre, The Lund Group, University of Copenhagen | Denmark | about 1 month ago
project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck: https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons/271052
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dose and patient burden. The project will include developing new visualisation strategies to aid delineation, as well as developing deep learning methods to enhance photon-counting CT images and better
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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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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