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relationships and provide mechanistic insights. Present results at conferences, prepare reports, and publish in peer-reviewed journals. Profile You have a PhD degree in environmental, polymer, organic, colloid
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 13 days ago
based on a combination of novel simulation techniques, Bayesian statistical methods and machine learning approaches. The successful candidate will work closely with Prof. Dr. Volker Springel, the director
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multidisciplinary collaboration, scholarly advancement, and the creation of tools and public goods.RequirementsApplicants must have (or expect to have at time of appointment) a PhD in the social sciences, statistics
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: What are efficient machine learning strategies to identify large ensembles of nanoparticles in tomograms (i.e., to identify nanoparticles on irregular 2D surfaces in 3D space)? What are appropriate
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research project Expertise in machine learning Additional expertise in one or more of the following: digital signal processing, statistics, multimodal processing, FAIR data management, music theory
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
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the Texas Children's Cancer Center. The project aims to develop and test novel CAR-redirected immunotherapy for pediatric solid tumors. In particular, we want to design and test a regulated PRDM1 knockdown in
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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Requirements REQUIRED: Ph.D. in Atmospheric science, Civil and Environmental engineering, mechanical engineering, or a related field; knowledge of and demonstrated skillset in physics-informed machine learning
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype