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on the development of algorithms for ECG signal analysis and validation of novel biodegradable ECG electrodes. The contract will be within the framework of the “Green Electrodes for Sustainable Electrophysiology
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using LLMs in therapeutic settings: data confidentiality, algorithmic biases, and limitations in contextual understanding. Study the acceptance of these tools by both patients and healthcare professionals
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Develop solutions to integrate large foundation models
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for fast tune-up Implementation and benchmarking of quantum algorithms Qualifications We are seeking candidates with: A PhD in Physics, Applied Physics, Nanotechnology, Computer Science, Engineering, or a
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this project, we will develop new algorithms and computational schemes as well as further develop existing computational frameworks in the team. We will focus on two related frameworks in the project
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: genetics, epigenetics, inflammation, metabolic pathology, autoinflammatory pathology, autoimmunity, arthritis, computational analysis, mathematical modeling, applied algorithms, machine learning in biology
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Biomedical Data Science Postdoc Appointment Term: 2 years (can be exended) Appointment Start Date: December 1, 2025 (Flexible) Group or Departmental Website: http://med.stanford.edu/summerhanlab.html (link is
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Fellow (EB1)1, available in https://dre.pt/application/conteudo/127238533 and in accordance with the consolidated version with the changes resulting from the update, approved on December 10, 2025 by
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Tenure-track Assistant and Associate Professorship positions in Algorithms at the Department of M...
We seek candidates to join the Algorithms Section. The focus of the call is both to expand at the new Vejle campus and to strengthen the Odense campus. We have several openings, and successful
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develops solutions for a range of vision tasks via machine learning and deep learning algorithms. The SSUDIO project aims to identify various objects of interest from shipboard 3D scans by training computer