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Key Functional Requirements & Qualifications: Advanced degree (PhD) in a field related to AI/ML or Data Analytics such as: Computer Science, Mathematics, Statistics, Physics, Biophysics, Computational Biology or Engineering Sciences.
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MS in Robotics, Computer Science, Software Engineering, Mechatronics, Electrical Engineering, Mathematics, or Physics. Strong engineering background from industry or school, ideally in areas/fields such as Robotics, Computer Science, Software Engineering, Mechatronics, Electrical Engineering, Mathematics, or Physics.
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Requirements Doctorate in Physics, Electrical Engineering, Applied Mathematics, or a related field. Applicants must have a broad knowledge of physics as it is often applied in a military or intelligence context, e.g. electromagnetic propagation and interaction with matter, aerodynamics, lasers, optics, etc.
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Ph. D., or Masters + 3 years experience, or Bachelor’s + 6 years experience in a computational discipline (e.g. Computational Biology, Bioinformatics, Biostatistics, Bioengineering, Computer Science, Mathematics, Physics, Statistics.
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PhD in computer science, applied mathematics, physics, computational biology or other quantitative disciplines. Expertise in at least one ML framework (PyTorch/TensorFlow/Jax) and robust experience in the Python data science ecosystem.
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PhD in applied mathematics, computer science, physics, computational biology or other quantitative disciplines. PhD in applied mathematics, computer science, physics, computational biology or other quantitative disciplines.
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Bachelor’s or higher degree in Computer Science, Mathematics, Physics, Quantitative Finance, Engineering, Statistics or equivalent field, with significant coursework or equivalent experience in programming, modeling, simulation and statistical techniques.
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You will need to thrive at the intersection of electrical design, software, firmware, PCB design, and sensor integration, grounded by a strong foundation in physics, mathematics, and core engineering disciplines.
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An advanced degree (master’s or doctoral) in a quantitative fields including but not limited to Computer Science, Applied Mathematics, Astro/Physics, Computational Chemistry, all relevant engineering and science disciplines.
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Develop cloud-native solutions to promote integration within the IT framework across various North American commodity markets, including energy, capacity, ancillary services and power & gas basis markets in the major ISO regions (PJM, CAISO, NYISO, MISO, SPP, ISO-NE, ERCOT, IESO, AESO.
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Degree in mathematics, computer science, engineering, physics, statistics, economics, computational sciences or a related quantitative discipline and 5+ years of analytical experience in an industrial or commercial setting OR PhD degree with at least 3+ years of industry experience.
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Bachelor's or Master's STEM degree - computer science, data science, economics, mathematics, actuarial sciences, engineering, or physics preferred. Experience in property insurance or catastrophe modeling industry a plus.
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Located in the heart of Boston, Northeastern University offers abundant opportunities for interdisciplinary collaboration, including colleagues in the Department of Physics, the Department of Mathematics, the Khoury College of Computer Science, and the Boston-area Institute for Artificial Intelligence and Fundamental Interactions.
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A PhD or master’s degree in computer science, computational statistics, mathematics, physics, engineering, optimization or related applied mathematics and technical disciplines as they pertain to data science/machine learning (ML.
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Additional areas of interest could include computational methods for genome assembly, population genetics, functional genomics, or structural variant interpretation in rare disease. The team member will focus on leading research projects related to analyses of long-read sequencing and structural variation in population studies such as the All of Us Research Program and the genome aggregation database (gnomAD), as well as rare disease programs including the NHGRI Mendelian genomics program (GREGoR) and the NICHD Fetal Genomics Consortium.
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