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Temporary Machine Learning Postdoc Jobs in New Jersey

Preferred 3+ years of professional or postdoctoral technical experience in the field of optical ... Machine Learning : Strong practical experience with state-of-the-art machine learning tools and ...

Preferred 3+ years of professional or postdoctoral technical experience in the field of optical ... Machine Learning : Strong practical experience with state-of-the-art machine learning tools and ...

... of machine learning algorithms for real-time signal analysis. The postdoc will collaborate in a multidisciplinary environment, managing laboratory resources and producing high-quality progress ...

Data Scientist I (Assistant)

Rahway, NJ · On-site

$104K - $114K/yr

Key Responsibilities Machine Learning Engineering & Molecular Modeling • Workflow Automation ... For temporary assignments lasting 13 weeks or longer, AllSTEM Connections is pleased to offer major ...

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Temporary Machine Learning Postdoc information

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

To thrive as a Temporary Machine Learning Postdoc, you need a PhD in a relevant field, a solid grasp of machine learning theory, and strong programming skills (often in Python or R). Experience with tools such as TensorFlow, PyTorch, and high-performance computing environments, as well as a record of peer-reviewed research, is typically required. Strong analytical thinking, collaboration, and effective communication help you stand out in this research-intensive role. These skills are essential for advancing cutting-edge research, publishing impactful findings, and contributing to interdisciplinary projects.

What types of projects and collaborations can a Temporary Machine Learning Postdoc expect to engage in during their appointment?

A Temporary Machine Learning Postdoc typically works on cutting-edge research projects, often contributing to ongoing studies or initiating novel investigations within the field. Collaboration is common, both within their immediate research group and with interdisciplinary teams, such as data scientists, domain experts, or industry partners. Postdocs may also mentor graduate students, present findings at conferences, and publish papers, gaining valuable experience that can lead to academic or industry roles. The environment is fast-paced and research-driven, offering opportunities for professional growth and expanding one's research portfolio.

What is a Temporary Machine Learning Postdoc?

A Temporary Machine Learning Postdoc is a fixed-term research position, typically held at a university or research institution, focused on advancing knowledge and techniques in machine learning. Postdoctoral researchers in this role work on specific projects, often collaborating with faculty, graduate students, or industry partners. The position is designed to provide advanced training and research experience after earning a PhD, usually lasting from several months to a couple of years. Temporary postdocs may contribute to publishing academic papers, developing algorithms, and mentoring students, while preparing for longer-term academic or industry careers.

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

AspectTemporary Machine Learning PostdocData Scientist
CredentialsPhD in Computer Science, Data Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often requires experience
Work EnvironmentAcademic or research institutions, labsCorporate, tech companies, startups
Employer & Industry UsageUniversities, research centersBusiness, technology, finance, healthcare
Search & Comparison IntentUnderstanding research-focused roles, academic opportunitiesIndustry roles, applied data analysis, business impact

The Temporary Machine Learning Postdoc is primarily research-oriented, often in academic or research settings, requiring a PhD. In contrast, a Data Scientist typically works in industry, applying data analysis and machine learning to solve business problems, often with a Bachelor's or Master's degree. Both roles involve machine learning skills but differ in environment, focus, and experience level.

What are the most commonly searched types of Machine Learning Postdoc jobs in New Jersey? The most popular types of Machine Learning Postdoc jobs in New Jersey are:
What are popular job titles related to Temporary Machine Learning Postdoc jobs in New Jersey? For Temporary Machine Learning Postdoc jobs in New Jersey, the most frequently searched job titles are:
What job categories do people searching Temporary Machine Learning Postdoc jobs in New Jersey look for? The top searched job categories for Temporary Machine Learning Postdoc jobs in New Jersey are:
What cities in New Jersey are hiring for Temporary Machine Learning Postdoc jobs? Cities in New Jersey with the most Temporary Machine Learning Postdoc job openings:
Infographic showing various Temporary Machine Learning Postdoc job openings in New Jersey as of June 2026, with employment types broken down into 56% Full Time, 22% Part Time, and 22% Temporary. Highlights an 95% In-person, and 5% Remote job distribution.

2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry

Princeton University

Princeton, NJ • On-site

$65K - $70K/yr

Full-time, Part-time

Posted 3 days ago


Princeton University rating

9.0

Company rating: 9.0 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

20th of 537 rated colleges and universities


Job description

The Skinnider Lab at Princeton University aims to recruit a postdoctoral fellow or more senior researcher to work on projects related to computational analysis of chemical and biochemical datasets. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available starting July 2025, and will remain open until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus. Many of the laboratory's interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict the existence of undiscovered small molecules that are likely to be observed by mass spectrometry. Of particular interest for this position is the identification of emerging illicit drugs, also known as novel psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user-friendly tools that will be used by a broad community. The scope of the work builds on recent publications from the laboratory, e.g. integrating language models with mass spectrometry data (https://www.nature.com/articles/s42256-021-00407-x, https://www.biorxiv.org/content/10.1101/2024.11.13.623458v1.abstract, https://www.nature.com/articles/s42256-024-00821-x, https://www.nature.com/articles/s42256-021-00368-1) or executing large-scale meta-analyses of mass spectrometric datasets (https://www.nature.com/articles/s41592-021-01194-4). The research is computational in nature but involves close interactions with experimental collaborators. Many of the problems are constrained by inherently low-quality or noisy data, and the successful candidate will be enthusiastic about contributing to data preprocessing and curation in addition to model development and evaluation. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve goals in the next stage of their career. The successful candidate will be motivated, independent, and have strong written communication skills. Candidates are required to have experience in one or more of the following areas as demonstrated through at least one first-author publication: computational biology/bioinformatics, cheminformatics, analytical chemistry/mass spectrometry/metabolomics, or machine learning/computer science. Term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending satisfactory performance and continued funding; those hired at more senior ranks may have multi-year appointments. Individuals should have or be expected to have a PhD with appropriate research experience in computational biology, chemistry, biochemistry, computer science, biological or chemical engineering, forensic science, or a related field. To apply online, please visit https://www.princeton.edu/acad-positions/position/38881 and submit a CV and cover letter. The cover letter should highlight 1-3 publications or preprints that you feel best address the requirement for experience in the above-mentioned areas. Please also include contact information for three references. Qualified candidates who pass an initial screening may be provided with short programming exercises to assess their skills. Only suitable candidates will be contacted. The work location for this position is in-person on campus at Princeton University. This position is subject to Princeton University's background check policy.
Expected Salary Range: $65,000 - $70,000
The University considers factors such as (but not limited to) scope and responsibilities of the position, candidate's qualifications, work experience, education/training, key skills, market, collective bargaining agreements as applicable, and organizational considerations when extending an offer. The posted salary range represents the University's good faith and reasonable estimate for a full-time position; salaries for part-time positions are pro-rated accordingly.
The University also offers a comprehensive benefit program to eligible employees. Please see this link for more information.

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