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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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experience. Applicants who are in the process of completing a Ph.D. degree will also be considered. Desirable qualifications include physics deep learning, digital twin, smart and resilient infrastructure
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docking to investigate viral spike/capsid architecture, thermal stability, and immune epitope accessibility. AI/Deep Learning: ML/DL model development (e.g., PyTorch, Lightning) for predicting antigenicity
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
computer vision and/or audio(-visual) machine learning (e.g., multi-object tracking, speaker/source localisation, multimodal fusion). Experience with deep learning frameworks (PyTorch or equivalent) and
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about applying deep learning to decode the regulatory grammar of plant genomes and translating predictions into testable biological hypotheses, we invite you to join the Omics Data Analysis and
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identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon the successful completion of a background
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Researcher will work in Professor Benjamin Peherstorfer’s group (https://cims.nyu.edu/~pehersto/ ) on scientific machine learning at the Courant Institute of Mathematical Sciences where they will help
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related field; Strong publication record in relevant venues; Solid programming skills in Python and a modern deep-learning framework Excellent communication skills in spoken and written English; Self
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for highly motivated postdoctoral candidates with a PhD in bioengineering deep knowledge in computational biology and machine learning. Candidates with a molecular biology or engineering degrees with