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(postdoc) Limited until: 30.04.2032 Reference no.: 5022 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re
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of equivalence with a German qualification. Skills and experience Extensive research experience in the field of artificial intelligence, machine learning, and deep learning with a focus on language
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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Faculty Positions in Genomics and Bioinformatics at the Institute for Genome Sciences, University of
translational biomedical research. We especially encourage applicants with research programs focused on developing machine learning / AI methods for bioinformatics, subclone and mutational analysis, genome
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—machine learning or AI applications in biomedical or omics contexts Excellent communication skills in English, particularly in clinical and biological settings, and the ability to work independently as
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immunology Experience with T cell engineering (CAR-T, TCR-T) and/or immunopeptidomics is preferred (but not required). At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and
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related field. The ideal candidate should have knowledge and/or experience in one or several of the following areas: Artificial Intelligence/Machine Learning algorithms and architectures Cybersecurity
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databases of observed ground motions, physics-based simulations of seismic waveforms and cutting-edge machine learning methods. This next generation of models should account for potential nonlinear site
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(thedonnellycentre.utoronto.ca ). Required Qualifications: We are looking for postdocs that have excellent molecular biology skills and/or a strong computational background including machine learning approaches. Candidates should
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for antibody repertoire sequencing and AI-driven research initiatives. Lead the integration of NGS into discovery workflows (in-vivo and in-vitro), emphasizing data generation for machine learning applications