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. QUALIFICATIONS PhD in Civil Engineering, Environmental Science, Computer Science, or a related field Research experience in hydrology, geospatial analysis, and machine learning Skills of scientific writing and
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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Mentor and Advise PhD thesis within the research group. Current topics include, reinforcement learning for tactile-aware manipulation skills, tactile sensing, mechanical intelligence for grasping, and
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages (e.g. R, Python) and an
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and machine learning systems led by Prof Christopher Summerfield. The post-holder will have responsibility for carrying out rigorous and impactful research into human-AI interaction and alignment, with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
solid academic background in polymer synthesis and electrochemistry. The candidates should be self-motivated to explore new areas of studies including AI related and machine learning fields, have evidence
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of Engineering, located in Brooklyn, NY, is deeply committed to excellence in teaching and learning. Tandon fosters student and faculty innovation and entrepreneurship that make a difference in the
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Job description: DESY The CMS Quantum Computing group develops generative machine learning models for detector simulations, specifically the simulation of showers in calorimeters: Proof-of-principle