Johns Hopkins Applied Physics Laboratory

Are you interested in exploring different complex systems domains such as climate science, earth systems, environmental resilience, materials science, and biology?. jhuapl.edu/isc), develops novel AI methods to transform climate intelligence and earth systems research, as well as accelerate scientific discovery and design for materials, protein engineering, and other physical and biological systems.. Have experience applying AI/ML to real-world data such as satellite imagery, remote sensing data, climate data, materials data, omics data, or additive manufacturing related data.. Are familiar with advanced AI methods like generative adversarial networks, physics-informed neural networks, neural operators, surrogate models, graph neural networks, and foundation models.. The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges.

artificial intelligence

<p>Description Do you love to use artificial intelligence (AI) and data analysis to process, analyze, and interpret data? Are you interested in exploring different complex systems domains such as climate science, earth systems, environmental resilience, materials science, and biology? If you are studying an AI-related field and interested in working at the intersection of complex systems, we'd love to meet you! We are seeking creative problem solvers with diverse capabilities and interests, as well as proven initiative, to join our team in taking on technical challenges in complex systems. The Complex Systems Group, within the Intelligent Systems Center (www.jhuapl.edu/isc), develops novel AI methods to transform climate intelligence and earth systems research, as well as accelerate scientific discovery and design for materials, protein engineering, and other physical and biological systems. As a researcher in our group, your primary responsibility will be contributing to and leading AI projects for complex systems. As a member of our research team, you will contribute to projects in a variety of ways, such as designing, training, and developing machine learning (ML) algorithms, collecting and preparing data, evaluating models, and programming. You will establish collaborations with complex system domain experts and seek opportunities to propose your ideas for internal or external funding. You will also establish working relationships with our Program Managers to identify opportunities to grow our work in key areas. You will publish your research in impactful venues and engage with the external research community, external collaborators, and sponsors. We are committed to developing exceptional talent and fostering a culture of innovation, and as part of our group you will have the chance to grow both within AI and data science, as well as in the complex systems we study. Qualifications You meet our minimum qualifications for the job if you: Have a PhD degree in a relevant field such as AI, Mathematics, Statistics, Computer Science, Data Science, or Engineering. Have published previous research in peer-reviewed journals and/or conferences. Have experience in one or more of our core skills: AI/ML algorithms, data science, deep learning, graph theory, software development, or statistical analysis. Are proficient in Python programming. Are able to obtain an Interim Secret security clearance by your start date and can ultimately obtain a Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. You'll go above and beyond our minimum requirements if you: Have background knowledge in earth systems, materials, biology, or other complex systems. Have experience in project management or leading technical teams. Have experience writing technical proposals, particularly government research sponsors. Have experience applying AI/ML to real-world data such as satellite imagery, remote sensing data, climate data, materials data, omics data, or additive manufacturing related data. Have proficiency in developing AI/ML software. Have experience with AI/ML and data science libraries such as PyTorch and the Hugging Face stack. Are familiar with advanced AI methods like generative adversarial networks, physics-informed neural networks, neural operators, surrogate models, graph neural networks, and foundation models. Why work at APL? The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation's most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work, continue to grow, and build strong connections with inspiring teammates. At APL, we celebrate our differences and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at www.jhuapl.edu/careers. About Us APL is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law. APL is committed to promoting an innovative environment that embraces diversity, encourages creativity, and supports inclusion of new ideas. In doing so, we are committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accommodations@jhuapl.edu. Only by ensuring that everyone's voice is heard are we empowered to be bold, do great things, and make the world a better place.Do you love to use artificial intelligence (AI) and data analysis to process, analyze, and interpret data? Are you interested in exploring different complex systems domains such as climate science, earth systems, environmental resilience, materials science, and biology? If you are studying an AI-related field and interested in working at the intersection of complex systems, we'd love to meet you! We are seeking creative problem solvers with diverse capabilities and interests, as well as proven initiative, to join our team in taking on technical challenges in complex systems. The Complex Systems Group, within the Intelligent Systems Center (www.jhuapl.edu/isc), develops novel AI methods to transform climate intelligence and earth systems research, as well as accelerate scientific discovery and design for materials, protein engineering, and other physical and biological systems. As a researcher in our group, your primary responsibility will be contributing to and leading AI projects for complex systems. As a member of our research team, you will contribute to projects in a variety of ways, such as designing, training, and developing machine learning (ML) algorithms, collecting and preparing data, evaluating models, and programming. You will establish collaborations with complex system domain experts and seek opportunities to propose your ideas for internal or external funding. You will also establish working relationships with our Program Managers to identify opportunities to grow our work in key areas. You will publish your research in impactful venues and engage with the external research community, external collaborators, and sponsors. We are committed to developing exceptional talent and fostering a culture of innovation, and as part of our group you will have the chance to grow both within AI and data science, as well as in the complex systems we study.</p>

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