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Machine Learning Computational Chemistry Jobs in Arizona

micro1 AI

AI Trainer - Microbiology Expert

Surprise, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Peoria, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Phoenix, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Tempe, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Glendale, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Yuma, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Buckeye, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Tucson, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

micro1 AI

AI Trainer - Microbiology Expert

Gilbert, AZ · Remote

$70 - $90/hr

Bachelor's degree or higher in Biology, Microbiology, Chemistry, or a related field. * Extensive ... Experience with AI, machine learning, or annotation projects related to biology or microbiology.

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Showing results 21-40

All JobsMachine Learning Computational Chemistry JobsMachine Learning Computational Chemistry Jobs in Arizona

Machine Learning Computational Chemistry information

Is computational chemistry in demand?

Computational chemistry is in high demand within industries such as pharmaceuticals, materials science, and chemical research, where it supports drug discovery and molecular modeling. Professionals with skills in machine learning, programming, and chemistry are increasingly sought after to develop advanced simulation tools and analyze complex data sets.

What is the difference between Machine Learning Computational Chemistry vs Computational Chemist?

AspectMachine Learning Computational ChemistryComputational Chemist
Required CredentialsAdvanced degrees in chemistry, computer science, or related fields; knowledge of machine learning and programmingDegree in chemistry, chemical engineering, or related fields; strong background in chemical theory and modeling
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm development and data analysisLaboratories, research institutions, industry; focus on chemical modeling and simulation
Employer & Industry UsageTech firms, pharmaceutical companies, research institutions applying AI/ML techniquesPharmaceutical, chemical, and materials industries conducting chemical research and development

Machine Learning Computational Chemists specialize in applying machine learning algorithms to chemical data, enhancing predictive models and simulations. Computational Chemists focus on traditional chemical modeling and simulations using computational methods. Both roles require strong chemistry backgrounds, but Machine Learning Computational Chemists emphasize data science and AI skills, while Computational Chemists focus on chemical theory and modeling techniques.

What is machine learning computational chemistry?

Machine learning computational chemistry is a field that combines machine learning techniques with computational chemistry to accelerate the discovery and design of molecules and materials. By training algorithms on large datasets of chemical information, researchers can predict molecular properties, simulate chemical reactions, and optimize compounds more efficiently than traditional methods. This approach helps reduce the time and cost required for research in drug discovery, materials science, and related fields.

What are some common challenges faced by professionals working in machine learning computational chemistry roles?

One common challenge in Machine Learning Computational Chemistry roles is integrating large and often complex chemical datasets with appropriate machine learning models, which requires a solid understanding of both domains. Professionals may also encounter difficulties in ensuring that their models are both interpretable and generalizable to new data, as overfitting is a frequent issue. Additionally, collaboration with chemists and data scientists is essential, so clear communication across disciplines is key to success. Staying up to date with the latest developments in both computational chemistry and machine learning is crucial for ongoing professional growth.

What are the key skills and qualifications needed to thrive as a machine learning computational chemist, and why are they important?

To thrive as a Machine Learning Computational Chemist, you need a solid background in chemistry, mathematics, and computer science, typically supported by an advanced degree in computational chemistry, cheminformatics, or a related field. Proficiency with programming languages (such as Python), machine learning frameworks (like TensorFlow or PyTorch), and molecular modeling software is essential. Strong analytical thinking, problem-solving skills, and effective collaboration are key soft skills that help drive innovation and teamwork. These skills and qualifications are critical for developing accurate models, advancing research, and translating computational insights into real-world chemical solutions.
What are popular job titles related to Machine Learning Computational Chemistry jobs in Arizona? For Machine Learning Computational Chemistry jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Machine Learning Computational Chemistry jobs in Arizona look for? The top searched job categories for Machine Learning Computational Chemistry jobs in Arizona are:
What cities in Arizona are hiring for Machine Learning Computational Chemistry jobs? Cities in Arizona with the most Machine Learning Computational Chemistry job openings:
Machine Learning Computational Chemistry jobs near you

Scientific Analyst II

University of Arizona

Tucson, AZ • On-site

Full-time

Re-posted 20 days ago

University Of Arizona rating

7.2

Company rating: 7.2 out of 10

Based on 67 frontline employees who took The Breakroom Quiz

383rd of 617 rated colleges and universities

Job description

Data Analysis and Machine Learning Pipeline Development:

  • Under moderate guidance collaborate in the design, develop, and execution of machine learning and AI-driven analytical pipelines to analyze large-scale biomedical datasets from UK Biobank, All of Us, Insight, and electronic medical records.
  • Apply supervised and unsupervised machine learning algorithms (e.g., logistic regression, random forests, deep learning) to identify risk factors, biomarkers, and patterns associated with neurodegenerative diseases and the effects of menopausal hormone therapy (MHT) on brain health.
  • Collaborate on the development and validation of predictive models integrating genomic, clinical, lifestyle, and imaging data using general knowledge of principals, theories and concepts.

Drug Repurposing Research and Bioinformatics Analysis:

  • Collaborating in computational drug repurposing analyses to identify existing FDA-approved compounds with potential efficacy for AD, PD, MS, and ALS prevention and treatment. Integrate multi-omics data (genomics, transcriptomics, proteomics) with clinical outcomes data to prioritize drug candidates.
  • Collaborate with wet lab and clinical teams to support translational interpretation of findings.

Epidemiological and Clinical Data Management and Harmonization:

  • Access, curate, harmonize, and manage large population-based datasets including UK Biobank, All of Us, and institutional EMR data.
  • Ensure data quality, reproducibility, and compliance with data use agreements and IRB protocols.
  • Collaborate in the develop and maintenance of reproducible data pipelines using Python, R, and high performance computer.
  • Perform statistical analyses including survival analysis, longitudinal modeling, and causal inference.

Scientific Communication, Dissemination, and Collaboration:

  • Compare and contribute to peer-reviewed manuscripts, conference presentations, and grant applications reporting research findings on MHT, menopause, and neurodegenerative disease.
  • Present results to interdisciplinary research teams, departmental seminars, and external stakeholders.
  • Collaborate closely with Dr. Francesca Vitali, co-investigators, and consortium partners. Maintain thorough documentation of analytical methods to ensure transparency and reproducibility.
  • Participate in lab meetings, journal clubs, and professional development activities.

Research Infrastructure and Continuous Improvement:

  • Maintain and improve lab computational infrastructure, including code repositories (GitHub), analytical workflows, and documentation standards.
  • Evaluate and adopt emerging AI/ML tools and methodologies relevant to brain science research.
  • Assist in training junior lab members or graduate students on data science methods and tools as needed.
  • Stay current with literature in neurodegenerative disease, computational.

Knowledge, Skills and Abilities:

  • Strong theoretical and applied knowledge of machine learning, deep learning, and statistical modeling.
  • Strong data wrangling and preprocessing skills for large, heterogeneous datasets.
  • Expert-level programming skills in Python and/or R; proficiency with ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Knowledge of drug repurposing methodologies or network pharmacology.
  • Knowledge and familiarity with electronic medical records data analysis.
  • Knowledge and proficiency with SQL and database management.
  • Ability to collaborate effectively within interdisciplinary teams spanning data science, neuroscience, clinical research, and epidemiology.
  • Ability to manage multiple concurrent projects and meet deadlines.
  • Ability to critically evaluate scientific literature and translate findings into research hypotheses and analytical strategies.
  • Ability to communicate complex analytical results clearly to both technical and non-technical audiences.

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