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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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volcanic arc using state-of-the-art deep learning techniques and modern seismological processing workflows, reporting to Dr Sacha Lapins. This role offers an exciting opportunity to process multi-year
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findings, and help attract funding. PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering Excellent coding skills Preferably a familiarity with machine learning and deep
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 19 days ago
-acid complexes. You will join the Delta team at Inria (Université de Lorraine), working closely with Dr. Yasaman Karami, expert in conformational dynamics, allostery, and deep learning for structural
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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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MD or Ph.D. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications PhD in computational science Working knowledge of machine learning and deep learning 3
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Applications to the Runway Startup Postdoc Program open on October 15 and close on February 15. Recently completed your PhD and want to harness your deep tech expertise to start a company? Want to
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. About You The ideal candidate will have a PhD in a numerate field, be familiar with medical image analysis using deep learning methods, have experience with GitHub and relevant programming languages
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. About You The ideal candidate will have a PhD in a numerate field, be familiar with medical image analysis using deep learning methods, have experience with GitHub and relevant programming languages
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application of scenario optimization techniques that could be applied in between shots and between run days, to include model correction techniques and transfer learning to maximize success probability of SPARC