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impactful system capable of reconstructing the 3D fetal aortic arch from routine 2D ultrasound views by combining generative modelling, deep learning, and rigorous clinical validation. Working within a
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the form of papers, posters or oral presentations Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow Downloading a copy of our Job Description Full details
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deep learning-based methods to identify novel small-molecule therapeutics. Tackling this challenge is critical for enabling more precise and efficient discovery efforts against complex biological targets
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in digital health, fitness, and rehabilitation. Preferred Qualifications Background in machine learning/deep learning is a plus. Equipment Utilized Physical Demands and Work Environment PHYSICAL
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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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/deep learning with GWAS/sequencing data and other types of omic data, as in Mendelian randomization and TWAS. In addition to new methods development and evaluations, the job responsibilities include
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numerical research. Your primary research project will focus on Naturally-occurring Deep Eutectic Systems (NADES) and their low temperature behavior, but you will have some latitude to study a variety of
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numerical research. Your primary research project will focus on Naturally-occurring Deep Eutectic Systems (NADES) and their low temperature behavior, but you will have some latitude to study a variety of
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The post-doctoral fellow will work with an interdisciplinary team of PIs and Co-Is within the Schulze Diabetes Institute. The projects focus on deep profiling of immune pathways that contribute