737 programming-"https:"-"FEMTO-ST"-"UCL"-"https:"-"https:"-"https:" Postdoctoral positions in United States
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Field
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in admissions, educational programs, or employment, including recruitment, hiring, promotions, transfers, discipline, terminations, wage and salary administration, benefits, and training. Students
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Science, or a related field Strong programming skills in Python, R Solid understanding of: Machine learning fundamentals Deep learning architecture (e.g., CNNs, RNNs, Transformers) Optimization and model
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culture techniques, DNA/RNA work, immunostaining, and animal husbandry (mice). Experience with endothelial biology and/or lipoprotein biology. Completion of a PhD program with an expected training time
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programming languages (e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability
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Sciences at Texas A&M University Texas A&M Forest Service Texas A&M Veterinary Medical Diagnostic Laboratory As the nation’s largest most comprehensive agriculture program, Texas A&M AgriLife brings together
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an outstanding research program in the field of flexible organic electronics, including sensing, bioelectronics, and organic semiconductor development. The Organic Materials and Devices (OMD) group seeks one
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that the university remains competitive with industry aligned practices, enhances collaboration with external partners, and supports the delivery of services and programs that meet professional and market-driven
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implementation of citizen science initiatives, including outreach programs, sample collection protocols, IRB compliance, participant recruitment, and data return strategies Collaboration with international
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an Individual Development Plan. This is a hybrid position with the possibility of remote work. Some travel will be required. Empty heading Application ProcessEmpty heading Postdoctoral researchers will work under
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programming skills and a keen interest in applying AI to biological systems, particularly in the context of clinical datasets and microbiome-driven disease mechanisms. 2. Immunology & Microbe–Immune System