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formulation, molecular-level characterization, and machine learning to develop predictive models linking chemical descriptors to adhesive properties. The work includes the development of a structured
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employing machine learning approaches to predict translational output. The postdoc will work closely with others of the team who will generate the data. Together we will be able to dissect conserved
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/jobs/363159/postdoc-on-causal-machine-learn… Requirements Additional Information Website for additional job details https://www.academictransfer.com/363159/ Work Location(s) Number of offers available
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in vivo perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
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Mathjobs.org | 27 days ago
focuses primarily on geometric analysis, spectral geometry, and differential geometry on singular spaces, preferably with connections to Finsler geometry, discrete geometry, or machine learning. Your
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focuses primarily on geometric analysis, spectral geometry, and differential geometry on singular spaces, preferably with connections to Finsler geometry, discrete geometry, or machine learning. Your
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Sofia University "St. Kliment Ohridski" | Santa Sofía, Coahuila de Zaragoza | Mexico | about 2 months ago
17 Aug 2026 Job Information Organisation/Company Sofia University "St. Kliment Ohridski" Research Field Computer science Researcher Profile Established Researcher (R3) Positions Postdoc Positions
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 10 hours agoprecision oncology: Build mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable
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big-data lake ecology research using techniques from statistics and computer science (e.g., statistical modeling, machine learning, data mining). Knowledge of open science tools (e.g., Github, Zenodo
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opens the door to many new experiments. Through the ErUM-Data and LEAPS-funded proposals, we develop resource-efficient and reliable Al and machine learning methods to advance ML for photon science