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for purposes of collective bargaining and matters affecting your compensation and working conditions. Basic Qualifications PhD in Computational Biology, Bioinformatics, Machine Learning, Data Science, or a
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, developing and applying AI technology to large medical images and genetic data for understanding brain development and aging, and precision psychiatry. In the past years, the laboratory has conducted various
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) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via cryospheric models. A
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. Table 1: Document list. Document Name Type of information Access Contents/Remarks Cover_Letter_name_of_applicant general general Signed cover letter Evidence of PhD thesis submission general general A
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into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor
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research questions. Strong quantitative research skills and proficiency in Python or R. Experience with large-scale textual data, natural language processing, machine learning, transformer-based models
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Experience in one or more of the following areas is preferred: Statistical genetics Human genetics Population genetics Evolutionary genetics Bayesian statistics Machine learning Large-scale genomic data
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will be preferred. Requirements: PhD degree in learning sciences, educational technology, human-computer interaction (HCI), information technology, AI or relevant fields Prior experience and proficiency
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Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 2 months ago
; and how these mechanisms can be understood, modelled and ultimately perturbed for biomedical discovery. Two scientific tracks Track 1: Computational Biology / Machine Learning for membrane protein