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experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Minimum Requirements: Ph.D. in applied
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active sites), in vitro and in vivo enzyme screenings, electrochemistry, and machine learning-assisted directed evolution. As part of this project, you will collaborate closely with PhD students and
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 26 days ago
arithmetic cores for FPGAs). The team hosts 6 faculty, 6 PhD students, 3 postdocs, 2 engineer, and multiple research interns. Additional information can be found on team website: https://team.inria.fr/emeraude
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, machine learning, and programming (preferably Python) is highly valued. Effective communication with clinicians and interdisciplinary researchers is crucial, and excellent proficiency in English is required
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applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data