What are the responsibilities and job description for the Postdoctoral Associate position at Upstate Medical University?
Job Summary
We invite applications for a Postdoctoral Associate role within our research team. Our interdisciplinary team is dedicated to pioneering advances in vision restoration, utilizing bioinformatics and cutting-edge machine learning techniques. This PostDoc will be integral to our mission, working alongside a diverse group of scholars ranging from undergraduate students to other PostDoctoral Fellows.
The successful candidate will possess a PhD in Neuroscience, Bioinformatics, Computer Science, or a related field. They will join ongoing efforts to decode and simulate visual information processing. The work encompasses a range of activities, from developing novel computational models to analyzing neural patterns associated with vision. The goal is to abstract complex neural data into a framework that can both predict and recreate visual experiences. This research has broad implications, from enhancing our understanding of the visual system to improving the lives of those with visual impairments through better therapeutic interventions. This is an opportunity to be at the forefront of a collaborative, innovative research environment that bridges the gap between bioinformatics and practical applications in vision science.
Minimum Qualifications
PhD in Neuroscience, Artificial Intelligence and Machine Learning, or related field. Two years of research experience in developing machine learning/deep learning solutions for multidisciplinary problems. Proven experience with analysis of physiological data using machine learning techniques. Publication record in journals and conferences. Proficiency in Python, with experience in libraries like Pytorch, Tensorflow, SciPy, and working in AWS environments. Demonstrated ability to develop machine learning pipelines and perform complex data analyses.
Preferred Qualifications
Experience working with multielectrode array data preferred, but not required.
Work Days
M-F
Message To Applicants
Recruitment Office: Human Resources
We invite applications for a Postdoctoral Associate role within our research team. Our interdisciplinary team is dedicated to pioneering advances in vision restoration, utilizing bioinformatics and cutting-edge machine learning techniques. This PostDoc will be integral to our mission, working alongside a diverse group of scholars ranging from undergraduate students to other PostDoctoral Fellows.
The successful candidate will possess a PhD in Neuroscience, Bioinformatics, Computer Science, or a related field. They will join ongoing efforts to decode and simulate visual information processing. The work encompasses a range of activities, from developing novel computational models to analyzing neural patterns associated with vision. The goal is to abstract complex neural data into a framework that can both predict and recreate visual experiences. This research has broad implications, from enhancing our understanding of the visual system to improving the lives of those with visual impairments through better therapeutic interventions. This is an opportunity to be at the forefront of a collaborative, innovative research environment that bridges the gap between bioinformatics and practical applications in vision science.
Minimum Qualifications
PhD in Neuroscience, Artificial Intelligence and Machine Learning, or related field. Two years of research experience in developing machine learning/deep learning solutions for multidisciplinary problems. Proven experience with analysis of physiological data using machine learning techniques. Publication record in journals and conferences. Proficiency in Python, with experience in libraries like Pytorch, Tensorflow, SciPy, and working in AWS environments. Demonstrated ability to develop machine learning pipelines and perform complex data analyses.
Preferred Qualifications
Experience working with multielectrode array data preferred, but not required.
Work Days
M-F
Message To Applicants
Recruitment Office: Human Resources
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