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computing, data science, or human-computer interaction. Programming skills needed to do data analysis (we mainly use Python) You can be interested either in theoretical methods development or applications
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postdoctoral fellow to join our research team focused on studying the metabolic and molecular mechanism of human pregnancy and its related complications using cutting-edge biological techniques. Characteristics
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research is understanding how viruses and bacteria interact with and subvert their human hosts to replicate and transmit, and how infection- and vaccine-induced immunity protects us in the face of new and
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The Ghezzi Lab at University of Massachusetts Lowell is looking for a full-time, fully benefitted Postdoctoral Research Associate to participate in a NIH-funded research program which aims
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job Title: Senior Postdoctoral
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SPECIFICS Postdoctoral Researcher in Defect Modeling The Pennsylvania State University Department of Nuclear Engineering The Computational Nuclear Materials Group (https://sites.psu.edu/cnmg/ ) at Penn State
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multidisciplinary team of clinicians, scientists, nurses and support staff, delivering a programme of research relating to traumatic injury, haemostatic resuscitation and optimisation of patient outcomes. The Centre
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Center for HIV Protein Interactions (https://www.hivppi.pitt.edu/ ). The U.S. National Institutes of Health-funded projects are focused on the design, development, and execution of computer simulations of
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Institute (CVRTI), is seeking a motivated postdoctoral fellow to work on projects that bridge from basic cell biology to small and large animal model systems. The focus of the lab is GJA1-20k with application
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of Biomedical Informatics (BMI) and the Pelotonia Institute for Immuno-Oncology (PIIO) are seeking a highly motivated Postdoctoral Scholar to work under the mentorship of Dr. Anjun Ma —a leader in deep learning