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Artificial Intelligence Machine Learning Physics information

What is artificial intelligence machine learning physics?

Artificial Intelligence Machine Learning Physics is an interdisciplinary field that applies AI and machine learning techniques to solve complex problems in physics. Experts in this area use algorithms to analyze large datasets, model physical phenomena, and accelerate scientific discoveries. The field combines knowledge of physics, computer science, and mathematics to design models that can predict, simulate, or interpret physical processes. Applications include materials science, quantum mechanics, astrophysics, and more, making it a rapidly growing area of research and industry.

What are the key skills and qualifications needed to thrive as an artificial intelligence machine learning physicist, and why are they important?

To thrive as an Artificial Intelligence Machine Learning Physicist, you need a strong background in physics, advanced mathematics, computer science, and experience with machine learning algorithms, typically supported by a graduate degree in a related field. Proficiency in programming languages such as Python or C++, machine learning frameworks like TensorFlow or PyTorch, and familiarity with data analysis tools are essential, along with experience in scientific computing. Critical thinking, problem-solving, and strong communication skills help you interpret complex data, collaborate across disciplines, and convey research findings effectively. These combined skills are crucial for developing innovative AI models, driving scientific discovery, and advancing technology at the intersection of physics and machine learning.

What collaborative projects can professionals in artificial intelligence machine learning physics expect to work on?

Professionals in Artificial Intelligence Machine Learning Physics often work on interdisciplinary teams, partnering closely with data scientists, physicists, and software engineers. They may contribute to projects such as developing advanced simulation tools, optimizing experimental data analysis, or creating machine learning models to predict physical phenomena. Collaboration is key, as these roles frequently involve integrating AI algorithms with physical models and leveraging domain-specific knowledge from physics experts. This dynamic environment fosters continual learning and offers opportunities to lead innovative research or transition into specialized engineering and research leadership roles.

What is the difference between Artificial Intelligence Machine Learning Physics vs Data Scientist?

AspectArtificial Intelligence Machine Learning PhysicsData Scientist
Required credentialsDegree in Computer Science, Physics, or related fields; certifications in AI/MLDegree in Statistics, Mathematics, Computer Science; certifications in data analysis
Work environmentResearch labs, tech companies, academia focusing on AI/ML applications in physicsBusiness, finance, healthcare sectors analyzing large datasets
Industry usageDeveloping AI models for physics simulations, research, and technologyExtracting insights from data to inform business decisions

Artificial Intelligence Machine Learning Physics and Data Scientist roles share a focus on data analysis and technical skills. However, AI/ML Physics emphasizes developing algorithms within physics contexts, while Data Scientists focus on analyzing diverse datasets across industries. Both roles often require similar educational backgrounds and certifications, but their applications and work environments differ significantly.

Is artificial intelligence and machine learning a good career?

Artificial Intelligence and Machine Learning are growing fields with high demand for skilled professionals, including roles like AI engineers and data scientists. These careers often require strong programming skills, knowledge of algorithms, and experience with tools like Python and TensorFlow. They offer competitive salaries and opportunities for innovation across various industries.

What are popular job titles related to Artificial Intelligence Machine Learning Physics jobs in Tennessee?

For Artificial Intelligence Machine Learning Physics jobs in Tennessee, the most frequently searched job titles are:

What job categories do people searching Artificial Intelligence Machine Learning Physics jobs in Tennessee look for?

The top searched job categories for Artificial Intelligence Machine Learning Physics jobs in Tennessee are:

What cities in Tennessee are hiring for Artificial Intelligence Machine Learning Physics jobs?

Cities in Tennessee with the most Artificial Intelligence Machine Learning Physics job openings:

Artificial Intelligence Machine Learning Physics jobs near you

Postdoctoral Scholar in Health AI-Pediatrics CBMI

The University of Tennessee

Memphis, TN

Full-time

Re-posted 11 days ago

Job description

THIS IS A GRANT-FUNDED POSITION FUNDED UNTIL OCTOBER 1, 2027.

The Postdoctoral Scholar designs, develops, evaluates, and optimizes advanced artificial intelligence (AI) methods that support intelligent cancer patient navigation and clinical decision support. Under the direction of the Principal Investigator, this position leads research and development on multimodal machine learning, agentic AI, explainable AI, causal inference, predictive analytics, and continuous learning to develop trustworthy AI solutions that improve cancer care delivery.

Ph.D. in a relevant discipline (e.g. Medical Informatics, Computer Science, Software Engineering, Mathematics, etc.) 

Track record of publications in top journals and conferences in the field. Strong track record of quantitative and analytics mastery, and expertise in Artificial Intelligence, Machine Learning, Causal Modeling, and Knowledge Graphs. Strong coding and implementation skills. Outstanding interpersonal skills and written and verbal communication capabilities.

WORK SCHEDULE: This position may occasionally be required to work weekends and evenings. May require occasional overnight travel. 

  1. Designs, develops, implements, and optimizes advanced artificial intelligence, machine learning, and multimodal AI models for intelligent cancer patient navigation and clinical decision support.

  2. Conducts research in explainable AI, agentic AI, causal inference, predictive analytics, knowledge representation, and continuous learning.

  3. Designs and evaluates AI algorithms using electronic health records, patient-reported outcomes, social determinants of health, medical imaging, and other healthcare data sources.

  4. Conducts benchmarking, validation, performance evaluation, and fairness, robustness, and explainability assessments of AI model.

  5. Collaborates with software engineers, clinicians, and interdisciplinary investigators to translate AI research into interoperable clinical applications and decision support tools.

  6. Develops and maintains reproducible analytical workflows and research software to support AI model development and evaluation

  7. Mentors graduate students and junior researchers throughout the project lifecycle.

  8. Prepares manuscripts, technical reports, conference presentations, and publications in leading journals and scientific meetings.

  9. Participates in proposal preparation and collaborative research activities supporting federally and state-funded research programs.

  10. Performs other duties as assigned.

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