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, Machine Learning , Neutrino , Neutrino physics and Astrophysics , Phenomenology , Quantum Field Theory , Theoretical Particle Physics , theory , Lattice QCD Appl Deadline: 2025/12/02 04:59 AM
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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UnitedKingdomTime) Position Description: Apply Position Description We invite talented and motivated applicants for PhD and Postdoc positions in the group of Haowei Xu (https://www.haoweixu.com/ ) at the Department
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of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment
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must be no earlier than 2024. You must have at least one first-author publication published by the time of starting the Postdoc. Application Please apply online at https://cemm.onlyfy.jobs/job/7k2624z5
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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical
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fields. Your research can be theoretical, applied, or span both. Deadline February 9, 2026. Where to apply Website https://jobrxiv.org/job/ellis-institute-finland-27778-postdocs-in-machine-learn