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Federated Learning Phd Jobs (NOW HIRING)

Jobgether

Requirements: * Advanced degree (PhD, MSc, or equivalent experience) in machine learning ... Experience in federated learning, distributed training, or privacy-preserving ML is considered a ...

New York University

POSTDOCTORAL ASSOCIATE

New York, NY · On-site

$62K - $67K/yr

Federated learning * Multimodal machine learning * Large language models * Power system ... PhD in Electrical Engineering or a related field by the start date of the position. We welcome ...

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Federated Learning Phd information

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How much do federated learning phd jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for federated learning phd in the United States is $32.69, according to ZipRecruiter salary data. Most workers in this role earn between $21.63 and $43.27 per hour, depending on experience, location, and employer.

What are some common challenges faced by a Federated Learning PhD in collaborative research environments?

As a Federated Learning PhD, you'll often work at the intersection of machine learning, privacy, and distributed systems, collaborating with interdisciplinary teams. A key challenge is addressing data heterogeneity and ensuring model robustness across diverse, decentralized datasets. Coordinating experiments across multiple organizations or devices while maintaining privacy and security can be complex. Effective communication and project management skills are essential to align research goals and integrate feedback from collaborators in academia or industry.

What is the difference between Federated Learning Phd vs Data Scientist?

AspectFederated Learning PhdData Scientist
Required CredentialsPhD in Computer Science, Machine Learning, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch-focused, often in academia or R&D departmentsIndustry settings, analytics teams, product development
Industry UsageSpecialized research projects, AI development, privacy-preserving MLData analysis, modeling, business insights, product optimization

Federated Learning Phds typically focus on advanced research in privacy-preserving machine learning, requiring a PhD and a strong background in AI. Data Scientists work across industries analyzing data to inform business decisions, often with a Bachelor's or Master's degree. While both roles involve machine learning, Federated Learning Phds are more research-oriented, whereas Data Scientists focus on applied data analysis.

What are the key skills and qualifications needed to thrive as a Federated Learning PhD, and why are they important?

To thrive as a Federated Learning PhD, you need deep expertise in machine learning, distributed systems, and data privacy, typically supported by a PhD in computer science or related fields. Proficiency with Python, TensorFlow, PyTorch, and specialized federated learning frameworks, as well as knowledge of secure aggregation and privacy-enhancing technologies, is essential. Strong research, problem-solving, and communication skills help you navigate complex challenges and collaborate effectively in academia or industry. These skills ensure the ability to advance federated learning techniques and deliver scalable, privacy-preserving AI solutions.

What is a Federated Learning PhD?

A Federated Learning PhD refers to a doctoral research position or program focused on federated learning, a machine learning approach where models are trained collaboratively across multiple devices or servers while keeping the underlying data decentralized and private. This research typically explores new algorithms, privacy-preserving techniques, system architectures, and applications of federated learning in fields like healthcare, finance, and edge computing. A PhD in this area involves both theoretical study and practical experiments, preparing graduates for advanced roles in academia or industry related to privacy-aware artificial intelligence.

Lead Data Scientist

Onyx Government Services,LLC

Suitland, MD • On-site

Full-time

Posted 6 days ago

Job description

About Us
Onyx Government Services, LLC., is a Service-Disable Veteran-Owned Small Business (SDVOSB), headquartered in Fairfax, Virginia. We specialize in data management, integration, and analysis solutions to provide decision-ready information to Command and Control (C2) and Decision Support Systems. We have demonstrated expertise in the field of Information Technology, database & COTS integration, and custom software development.
Onyx pairs subject matter and functional experts with developers to provide high quality, tailored solutions. In support of our various efforts, we have developed the Onyx Data Management Toolkit, a combination of Agile Development principles, COTS Integration, and custom software, to deliver flexible, cost-effective solutions to a variety of Department of Defense, Intelligence Community, and Law Enforcement agencies.
Job Summary
We are seeking a Subject Matter Expert (SME)-level Lead Data Scientist to leverage cutting-edge techniques to extract insights and patterns from large and complex datasets for the U.S. Census Bureau's Decennial Transformation and Application Modernization (DTAM) effort.
This role provides technical and management leadership on major advanced data science assignments, developing advanced algorithms, models, and frameworks using machine learning, deep learning, natural language processing, and generative AI / large language models.
The Lead Data Scientist ensures AI/ML products are safe, trustworthy, explainable, and compliant with the NIST AI Framework and Census Bureau policies. Decision-making and domain knowledge may have a critical impact on overall program implementation. May supervise others.
Work Location: Suitland, MD
Clearance: U.S. Citizenship required
***This position is contingent upon contract award. ***
Required Skills
  • Expert proficiency in Python, R, SAS, and SQL and associated data science libraries and tools
  • Demonstrated experience developing and operationalizing ML, deep learning, NLP, and generative AI / LLM models
  • Strong background in predictive modeling, time series analysis, anomaly detection, and feature engineering
  • Experience creating and using synthetic data and privacy-preserving data techniques
  • Working knowledge of the NIST AI Framework and responsible/ethical AI practices
Desired Skills
  • Experience transitioning data science capabilities from pilot to production (MLOps / LLMOps)
  • Familiarity with differential privacy, federated learning, and secure multi-party computation
  • Experience with large-scale federal statistical or survey data programs
  • Excellent written and verbal communication skills, including manuscript preparation and executive briefing to senior Government stakeholders
Education and Experience
  • PhD in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field - mandatory
  • 15+ years of experience providing technical and management leadership on major data science assignments (SME level)

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