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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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be found on our lab website: https://www.derosierelab.com/ Salary and benefits: ~€2300 net per month for candidates with less than 2 years post-PhD experience; €2450+ net per month for candidates with
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kinetic models and their hydrodynamic limits. Dispersive phenomena in kinetic theory. Quantum gases. Applicants must hold a PhD degree in mathematics, physics or a related field. We seek candidates with
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning
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. Applicants should hold a PhD in Computational Biology, AI/ML, Computer Science, Population Genetics, Bioinformatics, or a related field, and have fewer than five years of postdoctoral experience. Strong
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University of California Riverside School of Medicine | Riverside, California | United States | about 10 hours ago
methods in our research, with the goal of developing new therapies. The laboratory is currently supported by multiple grants from NIH to support these projects. Learn more about our research program by
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and able to manage your priorities. 🎓 We are looking for people with a PhD in machine learning, deep learning, data science, computer science, obtained less than 3 years before the date of hire, with a
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computational approaches to chart and understand the gene regulatory networks that are active in blood cells in health and disease. The successful candidates will have a PhD in Bioinformatics, Computational
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. The appointee is expected to reside in the Helsinki Metropolitan Area during the employment. QUALIFICATIONS AND EVALUATION Applicants are required to hold a PhD in a relevant field, such as memory studies
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not reached vulnerable groups or created impact at scale. SMARTSCALE brings together scientists, local partners, and governments to learn from past experiences and design better ways to spread moisture