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Weekend Machine Learning Postdoc Jobs in New York

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53.90K - $73.20K/yr

... researchers to join our team as Postdoctoral Scholars to work on NIH-funded research in ... A major component of this work will rely on applying machine learning methods to large-scale ...

Description POSTDOCTORAL ASSOCIATE New York University Tandon School of Engineering The Department ... of machine learning and AI. A strong record of publications and communication skills are also ...

Description POSTDOCTORAL ASSOCIATE New York University Tandon School of Engineering The Department ... machine learning as exemplified by a strong publication record. Previous experience on applied and ...

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53.90K - $73.20K/yr

The successful postdoc will lead epidemiology and computational investigations of how these ... Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in ...

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$72.50K - $80K/yr

This PostDoc position is available with Dr. Rosalind Wright's research group at the Icahn School of ... Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in ...

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53.90K - $73.20K/yr

This PostDoc position is available with Dr. Rosalind Wright's research group at the Icahn School of ... Biology, Machine Learning, or Bioinformatics, with relevant previous work and interest in ...

Postdoctoral Fellow-MSH

Manhattan, NY · On-site

$53.90K - $73.20K/yr

Postdoc fellows may work on many of the cutting-edge research areas at Mount Sinai, including: • ... machine-learning/deep-learning methodology research with application to biomedical data. • ...

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Weekend Machine Learning Postdoc information

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

To thrive as a Weekend Machine Learning Postdoc, you need a strong background in machine learning, statistics, and programming, typically supported by a PhD in a relevant field. Experience with tools such as Python, TensorFlow, PyTorch, and data analysis platforms, as well as familiarity with academic research methodologies, is essential. Exceptional problem-solving abilities, self-motivation, and effective communication are vital soft skills for success in research and collaboration. These skills enable you to drive innovative research, efficiently manage independent projects, and contribute meaningful insights to the field.

What are the typical projects and collaboration opportunities for a Weekend Machine Learning Postdoc?

As a Weekend Machine Learning Postdoc, you will often contribute to ongoing research projects, developing and refining machine learning models in collaboration with faculty, graduate students, and occasionally industry partners. While your hours are concentrated on weekends, you’ll typically participate in regular research meetings, code reviews, and may co-author papers or grant proposals. The role provides opportunities to mentor junior researchers and expand your expertise by working on interdisciplinary teams. This structure allows you to make significant research contributions while maintaining flexibility in your schedule.

What is a Weekend Machine Learning Postdoc?

A Weekend Machine Learning Postdoc is a postdoctoral researcher who focuses on machine learning projects and typically works on weekends or has a flexible schedule that includes weekend hours. This role often involves conducting advanced research in machine learning, developing algorithms, publishing papers, and collaborating with academic or industry teams. Weekend postdoc positions may be ideal for those balancing other commitments or seeking non-traditional work hours while continuing their research careers.

What is the difference between Weekend Machine Learning Postdoc vs Weekend Data Scientist?

AspectWeekend Machine Learning PostdocWeekend Data Scientist
Required CredentialsPhD in Computer Science, Machine Learning, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field
Work EnvironmentAcademic research settings, universities, research labsIndustry companies, startups, consulting firms
Employer & Industry UsageResearch institutions, universities, academic grantsTech companies, finance, healthcare, retail
Common Search & ComparisonYesYes

The Weekend Machine Learning Postdoc typically involves academic research with a focus on advancing machine learning theories and models, often requiring a PhD. In contrast, a Weekend Data Scientist applies data analysis and machine learning techniques in industry settings, often with a bachelor's or master's degree. Both roles may work on similar projects but differ mainly in their environment, credentials, and end goals.

What are the most commonly searched types of Machine Learning Postdoc jobs in New York? The most popular types of Machine Learning Postdoc jobs in New York are:
What are popular job titles related to Weekend Machine Learning Postdoc jobs in New York? For Weekend Machine Learning Postdoc jobs in New York, the most frequently searched job titles are:
What job categories do people searching Weekend Machine Learning Postdoc jobs in New York look for? The top searched job categories for Weekend Machine Learning Postdoc jobs in New York are:
What cities in New York are hiring for Weekend Machine Learning Postdoc jobs? Cities in New York with the most Weekend Machine Learning Postdoc job openings:
Infographic showing various Weekend Machine Learning Postdoc job openings in New York as of May 2026, with employment types broken down into 1% Locum Tenens, 58% Full Time, 36% Part Time, 4% Contract, and 1% Nights. Highlights an 84% Physical, and 16% Remote job distribution.
Postdoctoral Appointee - Artificial Intelligence for Lithium-Sulfur Batteries

Postdoctoral Appointee - Artificial Intelligence for Lithium-Sulfur Batteries

Argonne National Laboratory

Manhattan, NY • On-site

$72.88K - $121.47K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

The Chemical Sciences and Engineering Division seeks a Postdoctoral Appointee to conduct research focused on the development of high-energy, long-cycle-life lithium–sulfur batteries employing both liquid and solid-state electrolytes using artificial intelligence, under the guidance of a supervisor. The successful candidate will contribute to projects involving the synthesis, physical and chemical characterization, and electrochemical evaluation of sulfur cathodes in both coin-cell and pouch-cell formats. This role includes establishing correlations between the physical and chemical properties of sulfur cathodes, electrolytes and their electrochemical performance.

The candidate is also expected to have experience working with a range of liquid and solid electrolytes for lithium–sulfur battery systems. In addition, the postdoctoral appointee will perform advanced characterization of battery materials using synchrotron-based X-ray techniques to develop mechanistic understanding of material behavior and electrochemical performance. Responsibilities also include analyzing experimental data, preparing manuscripts for peer-reviewed journals, presenting research findings at scientific conferences, and preparing reports and presentations for group meetings.

Key Responsibilities Conduct research to develop high-energy-density, long-life lithium–sulfur batteries using liquid and solid-state electrolytes Synthesize and optimize sulfur cathode materials and related host structures Perform physical, chemical, and structural characterization of battery material Fabricate and evaluate lithium–sulfur cells in coin-cell and pouch-cell configurations Investigate the relationships between material properties, interfacial behavior, and electrochemical performance Work with a variety of liquid and solid electrolytes, including sulfide-based solid-state electrolytes Apply advanced characterization techniques, including synchrotron X-ray probes, to study battery materials and interfaces Conduct electrochemical testing, including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic cycling Analyze and interpret experimental data to provide insight into battery mechanisms and performance limitations Prepare technical reports, manuscripts, and scientific presentations for internal and external audiences Present research results at group meetings, scientific conferences, and other professional forums Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in fields of Chemistry, Chemical Engineering, Mechanical Engineering, Materials Science, or a closely related field Experience leveraging artificial intelligence or machine learning in the development of battery electrolytes and catalyst materials Demonstrated expertise in lithium–sulfur battery materials and electrolyte systems Strong skills in the synthesis and structural optimization of host materials for lithium–sulfur batteries Experience in the synthesis, processing, and characterization of solid-state electrolytes, particularly sulfide-based electrolytes, for energy storage applications Knowledge of interfacial engineering strategies, such as surface coatings, to suppress lithium dendrite formation Hands-on experience in the fabrication and testing of all-solid-state cells, including coin-cell and/or model cell configurations Knowledge of synchrotron-based X-ray characterization techniques, such as X-ray diffraction (XRD) and X-ray spectroscopy, particularly for in situ/operando studies of materials and interfacial chemistry during battery cycling Strong understanding of electrochemistry, with the ability to perform and interpret CV, EIS, and galvanostatic testing Experience processing, analyzing, and interpreting experimental results Demonstrated ability to work effectively on multiple concurrent projects Strong written and oral communication skills, with a record of peer-reviewed publications and recognition in a relevant research area Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork Job Family: Postdoctoral Job Profile: Postdoctoral Appointee Worker Type: Long-Term (Fixed Term) Time Type: Full time The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs.

Additionally, comprehensive benefits are part of the total rewards package. Click here (https://www.anl.gov/hr/healthcare-insurance) to view Argonne employee benefits! As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation.

Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A.

You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements.

Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment. #J-18808-Ljbffr