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Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 2 months ago
; and how these mechanisms can be understood, modelled and ultimately perturbed for biomedical discovery. Two scientific tracks Track 1: Computational Biology / Machine Learning for membrane protein
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and the Probabilistic Machine Learning Lab (https://www.helsinki.fi/en/researchgroups/probabilistic-machine-learning ; groups of Acerbi and Klami) at the Department of Computer Science, aligning with
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will have a strong research record in NLP, machine learning, or closely related areas, along with excellent communication and collaboration skills. We are especially interested in candidates with strong
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-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems. Prepare and draft technical reports, conference/journal papers, and
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Science at Johns Hopkins University seeks an outstanding postdoctoral fellow to lead scientific efforts on machine learning applications to signed languages to begin by Fall 2026. The fellow will work
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models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning
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community partners to ensure outputs reflect local priorities and inform adaptation planning. Duties may include: Develop spatially explicit computational models using machine learning, hydrologic, and energy
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extensive knowledge of robotic navigation, machine learning or other relevant fields experience working with marine robotic systems or the data they collect, including inertial, acoustic, visual and
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following skills and experience: A recent PhD graduate in computer science, engineering and supply networks, artificial intelligence, machine learning, or a closely related field. Knowledge of and experience
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, protein structure modelling, AlphaFold/multimer-based analyses, statistics, data visualization, and interdisciplinary work at the interface of proteomics, structures and machine learning. Track 2