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electrophysiology in brain slices and cell cultures. Performs behavioral tests in mice. Performs in vivo 2p imaging in head-fixed mice. Compiles and analyzes data with appropriate conclusions and recommendations in
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to understand the role of subcortical circuitry in normal and pathological feeding and reward processes. Applicant must hold a PhD or equivalent degree in neuroscience, biochemistry, molecular biology, psychology
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Knowledge, Skills, and Abilities PhD in Neuroscience is required. Experience with in vivo imaging in head-fixed behaving mice is required. Experience with Arduino microcontrollers for the design and control
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alignments, data acquisition, image processing and structure calculation using a variety of cryo-EM software. The candidate should be comfortable using Linux. Strong written and oral communication skills in
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culture, biochemistry, and cell imaging techniques are required. Certifications/Licenses Required Knowledge, Skills, and Abilities Must be able to work with rodent animals and human embryonic cells. Must
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to study processes relevant to human reproduction using genetics, molecular biology, biochemistry, cell biology, and microscopy/imaging. A qualified candidate will be capable of working independently
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& Decision Neuroscience Lab (https://www.konovalab.com/) led by the Principal Investigator and located at the Rutgers Center for Advanced Human Brain Imaging Research (CAHBIR). The ADN Lab conducts decision
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Knowledge, Skills, and Abilities - Prior experience in somatosensory research. - Familiarity with advanced imaging, electrophysiological techniques, and behavioral assay methodologies. - Proficiency in using
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: Performs specialized research techniques and procedures related to phenotypic characterization of cardiovascular disease using available signal-based and imaging techniques. Compiles and analyzes data using
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related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and point cloud data processing, deep learning for time series data prediction