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for the design and optimization of advanced structural materials. [1] https://www.pepr-diadem.fr/projet/ammetis-2/ [2] https://www.pepr-diadem.fr/ Where to apply E-mail mohamed.jebahi@ensam.eu Requirements
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Posted by Kate Stuart on Wednesday, March 18, 2026 in Job Opportunities . The Cruchaga lab (https://cruchagalab.wustl.edu/ ) at Washington University School of Medicine has a
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with electrophysiology acquisition and analysis (for example spike sorting, LFP analysis, population analyses) Strong quantitative skills and programming experience (MATLAB and or Python), including
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
inform observable macroscopic properties as part of the activities of the UNC Superfund Research Program (SRP) (https://sph.unc.edu/superfund-pages/srp/). This work involves running, developing, and
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considered a plus Understanding of Python scripting is considered a plus. As a person you are a strong communicator and are comfortable solving complex problems. This will sometimes require contacting system
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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August 2026 (or later), with the possibility of renewal depending on performance and available funding. To apply, please upload the following materials on the following postdoctoral application https
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graduate student at the University of Michigan Available to attend lectures and lead lab sessions Demonstrated working knowledge of Python including programs of 100-500 lines of code. Have previously taken
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) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and
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). Develop robust, reproducible and reusable Python code for model training, inference, and large‑scale computational experiments. Run and manage high‑throughput workloads on HPC or cloud infrastructure