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at the intersection between analytical chemistry, chemometrics and life sciences. As a postdoc in this project you will learn to use and help to develop cutting-edge methodologies linked to vibrational spectroscopy and
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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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, Electrical Engineering, Aerospace Engineering or a related field, with a focus on Robotic Perception and learning based methods Demonstrated expertise in at least one of the following areas: Machine Learning
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in this project you will learn to use and help to develop cutting-edge methodologies linked to vibrational spectroscopy and multivariate analysis and apply these to research questions within
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, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment / assignment relevant
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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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electrical engineering, control engineering, computer science, applied mathematics or a related field. The successful candidate will have expertise in at least in one of: Machine learning in the context
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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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. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large
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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