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Machine Learning Astronomy Jobs in New Jersey (NOW HIRING)

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Machine Learning Astronomy information

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?

AspectMachine Learning AstronomyData Scientist
Required CredentialsDegree in Astronomy, Physics, or related fields; knowledge of machine learningDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentResearch institutions, observatories, academiaCorporate, tech companies, consulting firms
Industry UsageAnalyzing astronomical data, developing models for celestial phenomenaBusiness 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 New Jersey?

For Machine Learning Astronomy jobs in New Jersey, the most frequently searched job titles are:

What job categories do people searching Machine Learning Astronomy jobs in New Jersey look for?

The top searched job categories for Machine Learning Astronomy jobs in New Jersey are:

What cities in New Jersey are hiring for Machine Learning Astronomy jobs?

Cities in New Jersey with the most Machine Learning Astronomy job openings:

Machine Learning Astronomy jobs near you
Infographic showing various Machine Learning Astronomy job openings in New Jersey as of June 2026, with employment types broken down into 1% Internship, 40% Full Time, 57% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Postdoctoral Associate - Astrophysics (Rutgers-CCA Flatiron Research Fellow)

Rutgers University

Piscataway, NJ • On-site

$63K/yr

Full-time

Medical, Dental, Life, Retirement, PTO

Re-posted 25 days ago

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Company rating: 7.7 out of 10

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Job description

Position Details
Position Information
Recruitment/Posting Title
Postdoctoral Associate - Astrophysics (Rutgers-CCA Flatiron Research Fellow)
Department
Physics & Astronomy
Salary Details
minimum salary per Postdoc Union Contract - $63,968
Offer Information
The final salary offer may be determined by several factors, including, but not limited to, the candidate's qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university's broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.
Benefits
Rutgers offers a comprehensive benefits package to eligible employees, based on position, which includes:
  • Medical, prescription drug, and dental coverage
  • Paid vacation, holidays, and various leave programs
  • Competitive retirement benefits, including defined contribution plans and voluntary tax-deferred savings options
  • Employee and dependent educational benefits
  • Life insurance coverage
  • Employee discounts programs

For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview.
Posting Summary
The Rutgers University Department of Physics and Astronomy invites applications for a postdoctoral research position starting on, or after, 7/01/2026. The position is to be offered jointly with the Center for Computational Astrophysics (CCA) at the Simons Foundation Flatiron Institute in New York City. For full consideration, applicants are encouraged to submit their materials by January 10, 2026.
The successful applicant will work at the galaxy-interstellar medium interface under the guidance of Professor Blakesley Burkhart, in collaboration with other members of the CCA and Rutgers science staff, and contribute to a research program on star formation, galaxy formation, the interstellar medium, and fluid dynamics. The successful candidate will be expected to work on all or a subset of the above topics, be proficient in working with large data-sets (observational or numerical), machine learning, and running/analyzing numerical simulations.
The initial appointment will be for two years at Rutgers University followed by a third year appointment held at CCA as a Flatiron Research Fellow. There is a possibility of an additional year's extension at CCA, contingent on performance. The successful candidate will benefit from the rich scientific atmospheres at Rutgers and CCA. Weekly visits between the scientific communities at Rutgers and CCA are highly encouraged. The postdoc will have opportunities to participate in observational work, help mentor graduate students, attend journal clubs/coffee meetings/colloquia at CCA and Rutgers. Funds for conference travel and professional development will be provided.
Position Status
Full Time
Posting Number
25FA1006
Posting Open Date
Posting Close Date
Qualifications
Minimum Education and Experience
Applicants must have a Ph.D. in astronomy or physics by the start date.
Certifications/Licenses
Required Knowledge, Skills, and Abilities
The successful applicant will contribute to a research program on star formation, galaxy formation, the interstellar medium and fluid dynamics. The successful candidate will be expected to work on the above topics, explore new opportunities on nearby galaxies with large data-sets, and develop and lead their own projects within this framework.
Preferred Qualifications
Equipment Utilized
Physical Demands and Work Environment
Overview
Statement
Posting Details
Special Instructions to Applicants
Quick Link to Posting
https://jobs.rutgers.edu/postings/261213
Campus
Rutgers University-New Brunswick
Home Location Campus
Busch (RU-New Brunswick)
City
Piscataway
State
NJ
Location Details
Pre-employment Screenings
All offers of employment are contingent upon successful completion of all pre-employment screenings.
Immunization Requirements
Under Policy 100.3.1 Immunization Policy for Covered Individuals, if employment will commence during Flu Season, Rutgers University may require certain prospective employees to provide proof that they are vaccinated against Seasonal Influenza for the current Flu Season, unless the University has granted the individual a medical or religious exemption. Additional infection control and safety policies may apply. Prospective employees should speak with their hiring manager to determine which policies apply to the role or position for which they are applying. Failure to provide proof of vaccination for any required vaccines or obtain a medical or religious exemption from the University will result in rescission of a candidate's offer of employment or disciplinary action up to and including termination.
Equal Employment Opportunity Statement
It is university policy to provide equal employment opportunity to all its employees and applicants for employment regardless of their race, creed, color, national origin, age, ancestry, nationality, marital or domestic partnership or civil union status, sex, pregnancy, gender identity or expression, disability status, liability for military service, protected veteran status, affectional or sexual orientation, atypical cellular or blood trait, genetic information (including the refusal to submit to genetic testing), or any other category protected by law. As an institution, we encourage all qualified applicants to apply. For additional information please see the Non-Discrimination Statement at the following web address: http://uhr.rutgers.edu/non-discrimination-statement

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