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29th June 2026 Languages English English English The Department of Mechanical and Industrial Engineering has a vacancy for a PhD Candidate in Machine Learning Approaches to Conservation Condition
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/Province Bavaria City München Website https://www.gov.sot.tum.de/en/innotech/public-policy-governance-and-innovative-technology/ https://www.tum.de/en/ Street Richard-Wagner-Straße 1 Postal Code 80333 E-Mail
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Job Description Apply now Req Id: 41855 Job Title: Post Doc Research Assistant City: West Lafayette Job Description: Job Summary The Department of Philosophy at Purdue University invites
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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are looking for candidates interested in developing new machine learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
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and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and electrotactile