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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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propagation, electromagnetics, optimization, machine learning, and networking. Strong documented experience in these areas is commendable, particularly by having published your work. Candidates should have an
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engagement, or programming--are encouraged to apply, with opportunities to grow in other areas through collaborative mentorship and team-based learning. As part of Cornell University, a world-class research
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change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex
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position on GW data analysis using machine learning (ML) with expected starting date February 2026. The position focuses on using neural posterior estimation for tackling issues related to the analysis
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industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data
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, electromagnetics, optimization, machine learning, and networking. Strong documented experience in these areas is commendable, particularly by having published your work. Candidates should have an excellent mastering
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deformations in different experimental contexts (Task 1.2, Task 2.2 and Task 4.1 of the project). - Coordinating a sub-group of the team on a set of project tasks, Task 1.2, Task 2.2 and Task 4.1. - Conducting
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personalised, ethnically-stratified risk scores. This is a highly interdisciplinary project at the intersection of machine learning, health equity, and precision medicine. The successful candidate will join a
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sequencing and researching disease in patient cohorts, working with machine learning techniques and programming computers. The candidate will learn about different flavors of metagenomic sequencing, how