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2026 Group or Departmental Website: https://mellab.stanford.edu/(link is external) https://spoglab.stanford.edu/(link is external) How to Submit Application Materials: To apply, please submit
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models and analysis pipelines for real-time performance, enabling adaptive imaging and feedback control Design active-learning and retraining strategies to robustly generalize to new microbial communities
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. The postdoctoral associate will be expected to work both collaboratively and independently on research projects, advancing computational methods using machine learning, developing automated pipelines for data
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delves into software design concerns, focusing on the internal structure and evolution of AI-enabled components themselves, including the design of retrainable pipelines, model-wrapping services, LLM
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ERC-funded Postdoctoral Researcher (m/f/d) in Zebrafish Optogenetics & Single-cell Genomics (Berlin)
, across relevant developmental stages establish robust and scalable experimental pipelines enabling precise spatial and temporal perturbations combined with high-throughput molecular readouts collaborate
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modelling of high-dimensional datasets. Experience in basic computational methods (e.g. R, Python, and/or other programming languages) Experience in developing computational analysis pipelines Experience with
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analyst. More information: https://boccaralab.com Colourbox via Unsplash Colourbox Work Tasks Scientific Leadership and Lab Support (~70% of time) Complement the existing support structure of the group by
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linked to oral health outcomes and health disparities. Apply and develop bioinformatic pipelines, statistical models, and computational tools to assess associations between microbial composition, oral
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response and resistance. The group develops patient-derived models including patient-derived xenografts (PDX), organoids and molecular characterization pipelines to study tumour evolution and therapy
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, structured extraction of claims/evidence, reproducible AI-in-the-loop pipelines, or evaluation/verification of model-generated outputs) is preferred. The initial appointment is funded for one-year from