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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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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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postdocs positions in the Center for Basic Research in Program Verification (CPV) at Department of Computer Science, Aarhus University. The research topics at the Center for Basic Research in Program
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computational proteomics, bioinformatics, machine learning, computer science, chemometrics, analytical chemistry, or a closely related field Strong experience in machine learning and/or deep learning
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, and open scientific resources where relevant Required Qualifications Applicants must have: A PhD in computational proteomics, bioinformatics, machine learning, computer science, chemometrics
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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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. 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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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and
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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 Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages Background in machine learning or deep