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Machine Learning Astronomy Jobs in California (NOW HIRING)

Adidev Technologies Inc

Data Science

San Francisco, CA · On-site

... Astronomy or related scientific discipline Must have * Understanding of various machine learning algorithms (e.g. SVM, Random Forests, Gradient Boosting, Log-Log regression, XGBoost, Lasso, Ridge ...

Adidev Technologies Inc

Data Science

San Francisco, CA · Remote

... Astronomy or related scientific discipline Must have * Understanding of various machine learning algorithms (e.g. SVM, Random Forests, Gradient Boosting, Log-Log regression, XGBoost, Lasso, Ridge ...

Zefr, Inc

Senior Data Scientist

Marina Del Rey, CA · On-site

$200K - $225K/yr

... patented machine learning and AI technology (Cognition AI) to offer brands and agencies more ... Ray, Pandas, DBT, FastAPI, Airflow, Astronomer, DBOS * DevOps: Github Actions, Docker, Terraform ...

Caltech

Mechanical Engineer

Pasadena, CA · On-site

... learning, and individual growth. The qualified mechanical engineer will contribute to instrument ... astronomy, and cosmology. This full-time position is based on the Caltech campus in Pasadena ...

Caltech

Mechanical Technician

Pasadena, CA · On-site

Basic understanding of manufacturing processes and experience working closely with machine shops ... learning opportunities; quickly assimilate and apply new information. * Demonstrate Agility ...

Caltech

Electronics Engineer

Pasadena, CA · On-site

While fostering a community of respect, learning, and individual growth, the OIR group designs ... knowledge in astronomy, cosmology, and planetary science. Previous and anticipated projects ...

All JobsMachine Learning Astronomy JobsMachine Learning Astronomy Jobs in California

Machine Learning Astronomy information

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.

What are 5 potential jobs for astronomy?

Potential jobs for astronomy graduates include research scientist at observatories or universities, data analyst for space agencies, astrophysics researcher, science communicator or educator, and software developer for astronomical data analysis. These roles often require strong analytical skills, programming knowledge, and familiarity with telescopes or data processing tools.

How much do machine learning engineers make at NASA?

Machine learning engineers at NASA typically earn between $90,000 and $150,000 annually, depending on experience, education, and security clearance levels. Salaries may also vary based on location and specific project responsibilities, with some roles requiring expertise in data analysis, programming, and scientific computing tools.

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 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.

Does NASA have machine learning engineers?

NASA employs machine learning engineers to develop algorithms for data analysis, spacecraft navigation, and scientific research. These roles often require expertise in programming, data science, and tools like Python and TensorFlow, with positions available through federal job portals and NASA's career website.

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.

Can AI replace astronomers?

Machine Learning Astronomers use AI to analyze large datasets, identify patterns, and make predictions about celestial phenomena. While AI can automate data processing and assist in research, it does not replace the need for human expertise in designing experiments, interpreting results, and making scientific judgments. The role of astronomers remains essential for guiding AI applications and advancing understanding of the universe.
What are popular job titles related to Machine Learning Astronomy jobs in California? For Machine Learning Astronomy jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Astronomy jobs in California look for? The top searched job categories for Machine Learning Astronomy jobs in California are:
What cities in California are hiring for Machine Learning Astronomy jobs? Cities in California with the most Machine Learning Astronomy job openings:
Machine Learning Astronomy jobs near you
Data Science

Data Science

Adidev Technologies Inc

San Francisco, CA • On-site

Full-time

Posted 13 days ago

Job description

Adidev Technologies Inc
www.adidevtechnologies.com
URGENT HIRE - HIRING PROCESS - 24-48 HOURS!
Adidev Technologies is seeking 1-2 yrs of relevant experience in Data Science. A project can last anywhere from 6 months to 18 months. Salary varies depending on experience, and we are in search of candidates looking to start as soon as possible. Excellent written and oral communication are required as is the ability to work well in a team environment.
If you are looking for a new challenge and are ready to make an impact on a growing team, then this will be a perfect fit. As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and debugging large-scale applications for one of our well-known clients.
Adidev Technologies is a growing software consulting company that is constantly expanding. As we are working with renowned clients and ready to take on new ones, we are seeking brilliant software engineers. Not only do we offer a great team to work with, but we also offer you an opportunity to make an immediate impact and get rewarded accordingly
Job Description
  • Demonstrated experience using machine learning, deep learning, statistical methodology, and simulation/optimization modeling in geospatial, network topography, recommendation systems, environmental systems, and/or agronomic problems.
  • Strong foundation in Python programming in a cloud environment.
  • Strong quantitative abilities, distinctive problem-solving, and excellent analysis skills
  • Expertise in data wrangling using SQL,
  • Practical knowledge and experience with cloud-computing systems and platforms, including the routine deployment of pipelines through Kubernetes
  • Fluency in querying/extracting/aggregating data via SQL scripting.
  • Extract, load and transform data (ETL) from structured and unstructured sources
  • Apply Natural Language Processing and Computer Vision to solve business use cases,
  • Strong skills in scientific data analyses, modeling, visualization and communication of results.
  • Knowledge of Python libraries (NumPy, Pandas, SciKit-Learn, TensorFlow, PyTorch), Spacy, MongoDB, PostgreSQL, Flask, streamlet and a good knowledge of data pipelines construction
  • Ph.D., M.S. or B.S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote Sensing Science, Environmental Sciences, Computational Astronomy or related scientific discipline

Must have
  • Understanding of various machine learning algorithms (e.g. SVM, Random Forests, Gradient Boosting, Log-Log regression, XGBoost, Lasso, Ridge, Clustering techniques, Neural Networks and others)
  • Regression (e.g. ? Linear/Logistic/MNL/Mixed Effects/Regularization)
  • Classification (K-means, Hierarchical, Latent Class, DBScan, SVM)
  • Dimension Reduction techniques (Principal Component analysis, Singular Value Decomposition etc.)
  • Optimization (Linear programming, Stochastic Gradient Descent, Genetic Algorithm etc.)
  • Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras
  • Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest
  • Distributed computing tools and cloud technology (AWS)

QUALIFICATIONS
  • Degree in Data Science, Computer Science, Engineering, Math, or Statistics preferred
  • At least 2 yrs of relevant experience in Data Science

SKILLS
  • SQL, statistical modeling, Feature engineering, Data visualization, Deploying models to production, Python programming, AWS, Domains(Healthcare/ Manufacturing/ Marketing/ Financial/ Telecommunication), powerbi/tableau, data warehouse

Benefits
  • Competitive Salary
  • Paid Relocation
  • Remote Support
  • Guaranteed Regular Salary Reviews
  • Job Type: W2 or Contract 1099 (full-time - 40 hours)

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