Computational Chemist: Thermodynamics for Industrial Minerals

Riven Systems; New York (NY); Hybrid; USD 160,000 - 210,000; Full time

Job summary

Riven Systems is seeking a Computational Chemist to translate thermodynamic first-principles into high-fidelity industrial predictions. You will collaborate with hydrometallurgy, process modeling, machine learning, and autonomous experimentation to build thermodynamic models with unprecedented accuracy.

The role favors interdisciplinary, hands-on contributors who can design multi-framework architectures, automate parameter regression against experimental data, and connect thermodynamics to

Qualifications

• Ph.D. or equivalent industry experience in a relevant field.
• Deep experience with solution thermodynamics frameworks (Pitzer, e-NRTL, MSE, SAFT, HKF, etc.).
• Experience regressing thermodynamic parameters against experimental data.
• Familiarity with hydrometallurgical unit operations and mass/energy balance.
• Ability to design and implement large software systems.
• Strong interdisciplinary communication and quick learning in new domains.
• Ability to work with a high degree of agency in a scrappy early-stage environment.

Responsibilities

• Own thermodynamic descriptions of aqueous electrolyte and mineral systems.
• Design multi-framework thermodynamic modeling architecture merging ML and EoS.
• Build automated pipelines for parameter regression against experimental data.
• Develop DOE approaches for driving automated lab experiments.
• Connect thermodynamics to multi-scale process models.

Skills

Thermodynamics modeling

ML integration

Software engineering

Interdisciplinary communication

Experimental data regression

Hydrometallurgy familiarity

Process modeling

Agile collaboration

Education

Ph.D. in Chemistry, Chemical Engineering, Materials Science, Geochemistry, or related field

Job description

Riven Systems is seeking a Computational Chemist to translate thermodynamic first-principles into high-fidelity industrial predictions. You will collaborate with hydrometallurgy, process modeling, machine learning, and autonomous experimentation to build thermodynamic models with unprecedented accuracy.

The role favors interdisciplinary, hands-on contributors who can design multi-framework architectures, automate parameter regression against experimental data, and connect thermodynamics to