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Trainee Ai Math Training Jobs in Indiana (NOW HIRING)

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Trainee Ai Math Training information

What is the difference between Trainee Ai Math Training vs Data Analyst?

AspectTrainee Ai Math TrainingData Analyst
Required CredentialsBasic math, programming fundamentals, training program completionBachelor's in Math, Statistics, or related field; sometimes certifications
Work EnvironmentTraining programs, workshops, entry-level projectsOffice setting, data analysis teams, project-based
Industry UsageEntry-level AI and machine learning developmentData-driven decision making across industries

While Trainee Ai Math Training focuses on foundational skills and entry-level AI-related training, Data Analysts apply these skills to interpret data and support business decisions. Trainee programs prepare individuals for roles like Data Analyst, but the roles differ in experience level and scope.

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Trainee Ai Math Training jobs near you

Principal Physics Consultant - AI Training

micro1 AI

Evansville, IN • Remote

$80 - $160/hr

Part-time

Posted 17 days ago

Job description

Role Title: Physics Expert (Professor / Principal Investigator)


Role Type: Contractor.


Location: Remote


micro1 is engaging Physics Experts—established Professors or Principal Investigators—to provide high-level domain guidance as part of an impactful project for a customer. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Adjudicate and render expert judgment on contested or competing physics arguments, solutions, or interpretations within your subfield.
  2. Compare alternative approaches to the same problem, detailing which is superior, under what assumptions, and in which regimes.
  3. Identify and articulate meta-level criteria for evaluating the robustness and validity of competing physics work, such as key assumptions and breaking points of approximations.
  4. Exercise calibrated confidence by providing authoritative assessments while transparently acknowledging genuine uncertainty or open questions in the field.
  5. Draft defensible written evaluations suitable for review by fellow senior physicists, ensuring clarity and rigor.
  6. Leverage technical tools such as LaTeX, SymPy, Python, and Jupyter to verify or contrast technical claims as needed.
  7. Clearly communicate when a question is unresolved within the field and delineate the pertinent considerations.


Preferred Qualifications

  1. PhD in physics with demonstrated expertise and scholarly impact in your specified subfield.
  2. Current or former Associate Professor, Full Professor, Chair Professor, or Principal Investigator/Group Leader with a track record of independent research leadership.
  3. Ongoing research activity in one or more of these areas: High Energy Physics, Mathematical Physics, Biophysics, Statistical Physics, Condensed Matter, AMO/Quantum Optics, Gravitation, Cosmology, Astrophysics, Quantum Information, or Optical Properties of Materials.
  4. 3–5 recent representative publications in your target subfield, with arXiv or DOI references.
  5. Prior experience supervising PhD students or postdocs, or equivalent leadership in industry research settings.
  6. Proficiency with LaTeX, SymPy, Python, and Jupyter (please indicate any gaps in experience with these tools).
  7. Exceptional written communication skills, with the ability to articulate nuanced and well-reasoned judgments.

Apply