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Field
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Cornell faculty members and the project’s co-principal investigators (from multiple organizations) to oversee a cross-organizational research team consisting of a group of postdoctoral associates, PhD
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challenges, encompassing both fundamental and applied research, from the development of algorithms, tools, and frameworks that advance scientific discovery to methodologies that utilise computational
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Immunobiology research. Develop software, creating query routines, and building relational databases. Possess advanced knowledge of algorithms used to solve problems based on biological information and data using
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intelligence is preferred in candidates applying for this position. Responsibilities Design, develop, test, and debug complex software programs. Implement and optimize algorithms (ML + AI) for data-intensive
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and manage multiple tasks; and • Excellent written and oral presentation skills for reaching audiences both in and out of one’s own field Key Responsibilities & Accountabilities 85% - Co-develop with
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learning. At least five (5) years of experience in AI/ML/DL or equivalent combination of education and experience. Demonstrated experience in algorithm development and structured programming ability
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powerful model platform, the lab aims to develop an integrative understanding of how animals adaptively respond to their sensory world, from molecular detection to the neural algorithms of complex motor
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well as develop and deliver advanced training. This is an excellent opportunity if you thrive on the opportunity for collaboration with complementary groups across PSC, particularly Computational Biology, User
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stakeholder groups. Involving multiple industry/corporate partners in each project and setting the proper expectations. Interviewing, selecting, and placing interns. Working with industry partners to prepare
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: Employing satellite observation and machine leaning to improve model representation of ice sheet surface melt”. The candidate will work under the direction of Prof Nowicki and in collaboration with multiple