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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 hour ago
, biophysics, or in a related interdisciplinary field. Experience with AI and machine learning tools is preferred. Applicant instructions: Applicants should be prepared to upload the following documents when
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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, computer science, or a related field. Expertise in causal inference and machine learning (in particular reinforcement learning), and strong experience with programming are desired.. Excellent communication and
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, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond. The particle physics phenomenology group members are: J. F. Kamenik (head), B. Bajc, S
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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate
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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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, fluorescence in situ hybridization, and bioinformatics. Preferred Qualifications Molecular biology and genetics laboratory experience; web computer skills; ability to learn new techniques and procedures
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Responsibilities Conduct and disseminate research on human-computer interaction, with interest and focus on considering learning aspects in design; or in educational technology with interest and focus on interaction
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landscapes. Collaborate with multidisciplinary teams to integrate remote sensing data with ground-based observations, physical algorithms, and machine learning models. Participate in field data collection and