ZipRecruiter

Log In

1

Machine Learning Astrophysics Jobs (NOW HIRING)

next page

Showing results 1-20

People also search for

All JobsMachine Learning Astrophysics Jobs

Machine Learning Astrophysics information

See salary details

$13

$22

$31

How much do machine learning astrophysics jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for machine learning astrophysics in the United States is $22.82, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.48 per hour, depending on experience, location, and employer.

How do professionals in machine learning astrophysics typically collaborate with domain experts and software engineers on research projects?

In machine learning astrophysics, collaboration is key to successful research outcomes. Professionals in this field often work alongside astrophysicists to ensure that data preprocessing and model outputs align with scientific objectives, while also partnering with software engineers to implement scalable and efficient algorithms. Regular meetings, code reviews, and joint data analysis sessions are common practices, enabling seamless integration of machine learning methods with domain expertise. This multidisciplinary teamwork helps address complex challenges, ensures scientific rigor, and accelerates the development of innovative solutions.

Will AI replace astronomy?

As a Machine Learning Astrophysics professional, AI is a tool that enhances data analysis and discovery in astronomy but is unlikely to fully replace human astronomers. AI automates tasks like data processing and pattern recognition, allowing scientists to focus on interpretation and theory development. Skills in programming, data analysis, and domain knowledge remain essential in this evolving field.

What is the difference between Machine Learning Astrophysics vs Data Scientist in Astrophysics?

AspectMachine Learning AstrophysicsData Scientist in Astrophysics
Required CredentialsPhysics or astrophysics degree, programming skills, machine learning knowledgeStatistics, computer science, or physics degree, programming, data analysis skills
Work EnvironmentResearch institutions, observatories, universitiesResearch projects, data analysis teams in academia or agencies
Industry UsageDeveloping models for astrophysical phenomenaAnalyzing astrophysical data sets for insights

Both roles involve data analysis and programming skills, but Machine Learning Astrophysics focuses on applying machine learning techniques to understand astrophysical phenomena, while Data Scientist in Astrophysics emphasizes broader data analysis and statistical methods within astrophysics research.

How much does NASA pay astrophysicists?

NASA astrophysicists are typically employed as federal government scientists, with salaries based on the General Schedule (GS) pay scale. Entry-level astrophysicists usually earn between GS-11 and GS-12, approximately $55,000 to $90,000 annually, with higher salaries for more experienced roles or those with advanced degrees and specialized skills. Salaries can also include benefits such as health insurance and retirement plans.

What is machine learning astrophysics?

Machine learning astrophysics is an interdisciplinary field that applies machine learning methods to solve complex problems in astrophysics. This involves using algorithms and statistical models to analyze vast amounts of astronomical data, identify patterns, classify celestial objects, and make predictions about cosmic phenomena. Researchers in this area work on projects such as detecting exoplanets, classifying galaxies, or predicting stellar evolution, often leveraging large datasets from telescopes and simulations. The integration of machine learning helps accelerate discoveries by automating data analysis and uncovering insights that would be difficult to find using traditional approaches.

What are the key skills and qualifications needed to thrive as a Machine Learning Astrophysics professional, and why are they important?

To thrive as a Machine Learning Astrophysics professional, you need a strong background in astrophysics, mathematics, and computer science, often supported by a relevant advanced degree such as a PhD. Proficiency with programming languages (like Python), machine learning frameworks (such as TensorFlow or PyTorch), and data analysis tools is essential. Critical thinking, problem-solving, and effective collaboration are vital soft skills for working with interdisciplinary teams and interpreting complex data. These capabilities are crucial for advancing research, uncovering new astrophysical phenomena, and efficiently analyzing large astronomical datasets.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 29 days ago

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)