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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
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Scene Understanding Detection and Identification of Objects (SSUDIO) project. The purpose of this project is to develop scene understanding from 3D scans of ships by applying machine learning/computer
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collection and employ relevant machine learning methods for data analysis and sensor fusion. The PhD Research Fellow will collaborate closely with another PhD Research Fellow at the Faculty of Health and
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compliance, if applicable). Does this position have supervisory responsibilities? No Preferred Education/Experience Bachelor’s degree in Computer Science, Machine Learning, AI, Data Science, Engineering
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 14 hours ago
Reponsibilities Perform analysis of large-scale quantitative and qualitative datasets as part of research projects. Use of new tools (machine learning, LLMs, OCR) to collect and process data. Work with bank and
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working with existing data pipelines written in SAS and other languages. These pipelines process very large datasets, and you maintain and adapt the scripts that run them. External collaborators develop
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the robot's physical embodiment suffer from poor generalization, weak explainability, and limited transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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Engineering and Mechatronics. The Proposed PhD thesis topic: “Intelligent Diagnostics of Electrical Machines through AI-Enabled IoT Systems: Design of Custom Embedded Hardware and Protocol-Aware Architectures