90 coding-"https:"-"FEMTO-ST"-"CSIC" "https:" "https:" "https:" "UCL" "UCL" research jobs at Northeastern University
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of emerging diseases. The successful candidate must be able to develop code to generate simulations and analyze large, complex datasets. They will be expected to carry out independent research and analysis, as
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and ML pipelines for drug synergy, write code for data analysis and post-processing data. Training of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is
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https://hr.northeastern.edu/benefits/ for more information. All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color
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applications for the position of Research Assistant in Invasive Pest Genomics. The intern will contribute to the efforts of an NSF-funded project (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2418203
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical
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assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information. All qualified applicants are encouraged to apply and will
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biochemistry, genetics, microbiology, and cell biological approaches. For more information check the Saavedra lab website: https://www.saavedralab.com The successful candidate for this role will join a
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, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information. All qualified
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eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits
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for epidemiology with some coding of simulations. Tentative start date: February 2026. This work will contribute towards the understanding of disease progression and building reinforcement learning frameworks