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Temporary Machine Learning Postdoc Jobs (NOW HIRING)

The Pennsylvania State University

POSITION SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania ...

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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.

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Temporary Machine Learning Postdoc jobs near you

Postdoc in AI-driven road network performance prediction and maintenance.

KTH Royal Institute of Technology

Stockholm, ME • On-site

$45K - $61K/yr

Other

Posted 5 days ago

Job description

Postdoctoral Position in Ai-driven Road Network Performance Prediction and Maintenance

The Division of Highway and Railway Engineering at KTH Royal Institute of Technology, School of Architecture and Built Environment is seeking a motivated and collaborative postdoctoral for a project on developing machine learning tools for pavement management. The project is conducted in collaboration with the Swedish Transport Administration and partners from the road industry.

The project aims to create data-driven decision support tools for predicting pavement performance and planning maintenance and reinforcement. It will build on extensive pavement performance datasets and apply advanced machine learning and statistical methods to generate predictions and assess the effectiveness of different maintenance and reinforcement strategies. The postdoc will lead work on extracting and analyzing pavement condition and traffic data, integrating them with climate and environmental datasets, and developing validated predictive models of pavement deterioration.

The position is research-focused, with opportunities to strengthen independence and build qualifications for future academic or industry careers. Teaching at various academic levels may also be included. The work will take place in a multidisciplinary environment combining expertise in pavement engineering, mechanics of pavement materials and structures, and machine learning. The role offers opportunities to contribute to high-impact publications and to practical decision support tools for the road engineering community.

Qualifications

Requirements

  • A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
  • PhD in Civil Engineering, Mechanical Engineering, Data Science, Machine Learning, or a closely related field.
  • Programming skills in Python, R, or similar data science languages

Preferred qualifications

  • A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
  • Strong background in machine learning, statistical modeling, and big-data analytics.
  • Experience with infrastructure or transportation data (pavement, traffic, weather, or similar)
  • Knowledge of pavement engineering and pavement deterioration modeling is a merit.
  • Ability to work both independently and collaboratively in a multidisciplinary setting.
  • Good communicative skills in English, both spoken and written, as it is required in the daily work.
  • Teaching experience is an advantage.
  • Awareness of diversity and equal opportunity issues, with a focus on gender equality.

Great emphasis will be placed on personal skills.

Apply