Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... Machine learning or probabilistic modeling * Quantum computing or advanced optimization techniques
Quick apply
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... Machine learning or probabilistic modeling * Quantum computing or advanced optimization techniques
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... machine learning, or geometric/topological approaches) to improve risk analytics Required ...
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... machine learning, or geometric/topological approaches) to improve risk analytics Required ...
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... Machine learning or probabilistic modeling * Quantum computing or advanced optimization techniques
Quick apply
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... Machine learning or probabilistic modeling * Quantum computing or advanced optimization techniques
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... Machine learning or probabilistic modeling * Quantum computing or advanced optimization techniques
Quick apply
Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics ... Machine learning or probabilistic modeling * Quantum computing or advanced optimization techniques
... astrophysics, quantum computing, biotech and other data-driven fields--who are interested in ... Machine learning (introductory) * Computational or applied problem-solving What You'll Gain * Hands ...
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... astrophysics, quantum computing, biotech and other data-driven fields--who are interested in ... Machine learning (introductory) * Computational or applied problem-solving What You'll Gain * Hands ...
... astrophysics, quantum computing, biotech and other data-driven fields--who are interested in ... Machine learning (introductory) * Computational or applied problem-solving What You'll Gain * Hands ...
Quick apply
... astrophysics, quantum computing, biotech and other data-driven fields--who are interested in ... Machine learning (introductory) * Computational or applied problem-solving What You'll Gain * Hands ...
... astrophysics, quantum computing, biotech and other data-driven fields--who are interested in ... Machine learning (introductory) * Computational or applied problem-solving What You'll Gain * Hands ...
Quick apply
... astrophysics, quantum computing, biotech and other data-driven fields--who are interested in ... Machine learning (introductory) * Computational or applied problem-solving What You'll Gain * Hands ...
Data Engineering and Analytics Manager
Tewksbury, MA · On-site +1
... defense, astronomy, ophthalmic products, telecommunications components and cover glass that is ... Familiarity with advanced analytics, machine learning, or AI data preparation requirements.
Data Engineering and Analytics Manager
Tewksbury, MA · On-site +1
... defense, astronomy, ophthalmic products, telecommunications components and cover glass that is ... Familiarity with advanced analytics, machine learning, or AI data preparation requirements.
Machine Learning Astronomy information
See Cambridge, MA salary details
$27.9K - $34.1K
5% of jobs
$36.2K is the 25th percentile. Wages below this are outliers.
$34.1K - $40.3K
59% of jobs
$40.3K - $46.5K
9% of jobs
$47K is the 75th percentile. Wages above this are outliers.
$46.5K - $52.7K
17% of jobs
$52.7K - $58.9K
4% of jobs
$58.9K - $65.1K
2% of jobs
$65.1K - $71.3K
3% of jobs
$71.3K - $77.6K
0% of jobs
$77.6K - $83.8K
0% of jobs
$83.8K - $90K
0% of jobs
$90K - $96.2K
0% of jobs
$27.9K
$46.5K
$96.2K
How much do machine learning astronomy jobs pay per year?
As of Sep 7, 2026, the average yearly pay for machine learning astronomy in Cambridge, MA is $46,543.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,500.00 and $50,300.00 per year, depending on experience, location, and employer.
What is machine learning astronomy?
Machine learning astronomy is the application of machine learning techniques to analyze and interpret astronomical data. This field combines computer science, statistics, and astronomy to automate tasks such as classifying celestial objects, detecting anomalies, and predicting astronomical events. With the increasing volume of data from telescopes and space missions, machine learning helps astronomers process and extract meaningful insights more efficiently. Researchers in this area develop algorithms that can learn patterns from vast datasets, leading to new discoveries and a deeper understanding of the universe.
What are some common challenges faced by professionals working in machine learning astronomy?
Machine learning astronomers often encounter challenges such as handling extremely large and complex datasets, ensuring data quality, and effectively preprocessing astronomical data to reduce noise and artifacts. Additionally, interpreting model results in a scientific context can be demanding, as it requires both technical expertise and domain knowledge. Collaboration with astronomers, data engineers, and software developers is essential to ensure that machine learning models are both accurate and scientifically meaningful.
What are the key skills and qualifications needed to thrive as a machine learning astronomer, and why are they important?
To thrive as a Machine Learning Astronomer, you need a strong background in astrophysics, statistical analysis, and programming (often with a PhD in a related field). Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and astronomical data systems is essential. Critical thinking, problem-solving, and effective collaboration are key soft skills for innovating solutions and working within research teams. These skills enable the effective analysis of large astronomical datasets, driving new discoveries and advancements in the field.
What is the difference between Machine Learning Astronomy vs Data Scientist?
| Aspect | Machine Learning Astronomy | Data Scientist |
|---|---|---|
| Required Credentials | Degree in Astronomy, Physics, or related fields; knowledge of machine learning | Degree in Computer Science, Statistics, or related fields; strong programming skills |
| Work Environment | Research institutions, observatories, academia | Corporate, tech companies, consulting firms |
| Industry Usage | Analyzing astronomical data, developing models for celestial phenomena | Business analytics, predictive modeling, data visualization |
Machine Learning Astronomy focuses on applying machine learning techniques to astronomical data within research settings, while Data Scientists work across various industries analyzing data to inform business decisions. Both roles require strong analytical skills and programming knowledge but differ in domain focus and work environment.
How is machine learning used in astronomy?
Machine learning astronomy involves applying algorithms to analyze large datasets from telescopes and space missions, enabling tasks such as identifying celestial objects, classifying galaxies, detecting exoplanets, and predicting cosmic phenomena. Professionals in this field often use tools like Python, TensorFlow, and data analysis techniques to interpret complex astronomical data efficiently.
What are popular job titles related to Machine Learning Astronomy jobs in Cambridge, MA?
For Machine Learning Astronomy jobs in Cambridge, MA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Astronomy jobs in Cambridge, MA look for?
The top searched job categories for Machine Learning Astronomy jobs in Cambridge, MA are:
Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics
Cambridge, MA • On-site
Full-time
Re-posted 2 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)