Deep Learning Application Research

As a Deep Learning Application Researcher at Peraton, your primary responsibilities will involve developing scalable solvers for differential equations by leveraging Deep Learning techniques applied to Stochastic Differential Equations. You'll take part in innovative research projects that seek to optimize the efficiency and performance of numerical models for Navy applications, ensuring they are less computationally intensive than traditional approaches like finite element and finite difference methods.

To qualify for the Deep Learning Application Research position at Peraton, candidates must hold a Ph.D. and possess at least 7 years of experience in relevant fields. Additionally, a strong understanding of recent Deep Learning advances and the mathematical theory behind Stochastic Differential Equations is essential. Proficiency with modern Deep Learning libraries and the ability to secure an Interim DoD SECRET Clearance are also necessary to succeed in this role.

Yes, the Deep Learning Application Research position at Peraton is primarily a remote role. However, it does require periodic travel to the NSWC Carderock facility in West Bethesda, MD for collaboration and project updates, allowing for a flexible work-life balance while contributing to meaningful projects.

For the Deep Learning Application Research position at Peraton, candidates should be well-versed in modern Deep Learning libraries such as TensorFlow, PyTorch, or similar frameworks. Familiarity with tools and libraries that facilitate the implementation of numerical methods and algorithms in deep learning will be beneficial in carrying out research and development tasks effectively.

The expected salary range for the Deep Learning Application Research position at Peraton is between $104,000 and $166,000. This range is indicative of the typical compensation for this position, reflecting the skills, experience, and qualifications of the applicants.

During the interview, be prepared to discuss specific projects where you utilized Deep Learning libraries such as TensorFlow or PyTorch. Highlight how you implemented models for various tasks, the challenges you faced, and how you overcame them. Mention any research or published work that showcases your proficiency in using these tools.

Your answer should cover the basic principles of Stochastic Differential Equations (SDEs) and discuss their significance in modeling uncertainties. Explain how these equations can be applied within Deep Learning frameworks to solve complex problems, particularly in the context of your role at Peraton, enhancing computational efficiency for Navy applications.

Demonstrate your knowledge by outlining an approach that may include using hybrid techniques involving both classical and DL methods. Discuss how you would evaluate performance metrics, optimize computational resources, and iterate on your design based on prototype results connecting to the goals at Peraton.

Think of a specific project that showcases your ability to implement deep learning solutions. Discuss the problem you addressed, the methodologies you employed, the outcomes of your work, and any impact it had on the organization's goals or objectives.

Share your methods for staying informed, such as following leading research journals, participating in professional organizations, and attending relevant conferences or seminars. Mention some recent advancements you’ve encountered and how they may influence your work at Peraton.

Discuss the balance between deep learning techniques and traditional numerical methods. Highlight potential synergies between the two and provide examples where integrating them can lead to improved efficiencies or novel solutions in engineering or computational problems.

Explain your approach to model debugging and optimization, focusing on techniques such as hyperparameter tuning, using validation datasets to avoid overfitting, and leveraging visualization tools to monitor model performance. Share your insights on the importance of iterative testing and validation in the optimization process.

Highlight your experience in interdisciplinary teams, your communication style, and how you ensure an effective exchange of ideas and feedback. Emphasize the importance of documenting processes and findings, as well as being open to collaboration for problem-solving.

Address the unique challenges faced in defense applications, such as data security, computational resource limitations, and the need for high levels of accuracy and reliability. Share your thoughts on potential solutions or adaptations that can be made to overcome these obstacles.

Express your enthusiasm for contributing to national security through innovative technology and research. Connect your personal goals and values with Peraton’s mission and explain how your expertise aligns with their vision, emphasizing your desire to be part of a team that tackles complex challenges.