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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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that currently lack effective treatments, such as Parkinsons Disease. By combining machine learning with quantum chemistry and structure based approaches, the project will accelerate the translation
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(analysis/ geometry), applieddifferential geometry and machine learning;•Solid publication record in image analysis, scientific computing or machine learning.•Knowledge and practical experience in at least 3
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Erasmus MC (University Medical Center Rotterdam); today published | Netherlands | about 12 hours ago
subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
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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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. Essential qualifications: PhD in Computer Science, Robotics, Machine Learning, or related fields. Programming skills in Python/C++ and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX). Strong
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Location: Cambridge, Massachusetts 02142, United States of America [map ] Subject Areas: Artificial Intelligence Biology Machine Learning Appl Deadline: (posted 2025/09/30, listed until 2026/03/30) Position
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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learning, and computer graphics. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100% for PhDs and TV-L E14, 100% for PostDocs; 45k – 57k