337 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" Fellowship positions
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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for drone swarms. The role will focus on multi-agent visual perception techniques. Group website: https://personal.ntu.edu.sg/wptay/ Key Responsibilities: Develop signal processing and machine learning
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team in the AI in Medicine Lab (www.aimlab.ca ). This position is based in the School of Biomedical Engineering. The successful candidate will work in the AI in Medicine Lab, applying machine learning
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with
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highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential
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veterinary technician simulation training as well as internal and external continuing education. This experience will provide the foundation necessary to: identify the learning needs of diverse audiences
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contribute to overall lab operations. The applicant will be a collaborative, impact-focused problem solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber
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status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment