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language processing and computer vision reveal the even greater potential of general-purpose foundation models. These models, pre-trained by SSL on diverse datasets and fine-tuned for specific tasks, offer superior
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neural circuit analysis with experience in: In vivo two-photon imaging or Electrophysiology in behaving animals Quantitative data analysis and computational modeling of network activity This is a full-time
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analysis Integration of molecular profiles with circuit data Development of reproducible pipelines for large-scale datasets CRISPR-based functional genomics This is a full-time position available from
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | about 1 month ago
» Veterinary medicine Researcher Profile Recognised Researcher (R2) Positions PhD Positions Country Austria Application Deadline 1 Nov 2025 - 23:59 (Europe/Vienna) Type of Contract Temporary Job Status Full-time
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bring for this position: PhD in a relevant subject, e.g. molecular biology, bioinformatics or related field Strong data analysis and coding skills in Python Experience with pediatric leukemias
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team for sample processing and wetlab assay optimization Data interpretation and reporting for drug screens on individual patients Your profile What you bring for this position: PhD in a relevant subject
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20 Sep 2025 Job Information Organisation/Company University of Salzburg Research Field Computer science Researcher Profile Recognised Researcher (R2) Country Austria Application Deadline 26 Oct 2025
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to join our team! Your professional field of activity: We are looking for a University Assistant Postdoc to to complement the research team around Univ.-Prof. Ulisse Stefanelli, PhD. The focus of our
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team! Your professional field of activity: We are looking for a University Assistant Postdoc to to complement the research team around Univ.-Prof. Ulisse Stefanelli, PhD. The focus of our research is on
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. The goal is to build foundation models capable of learning from richly structured or semi-structured data where traditional graph neural networks may fall short, enabling better representation, inference