Life Sciences Researcher | Remote

Crossing Hurdles; United States; A distancia; AUD 90.000 - 130.000; Jornada completa

Descripción de la vacante

Crossing Hurdles in Australia is seeking a scientifically skilled researcher to tackle complex problems within your field using Python and reproducible workflows. You will develop Python-based analyses, models, simulations, or data-processing pipelines and contribute to rigorous validation of results.

The role emphasizes clear code, transparent methodologies, and collaboration with interdisciplinary teams. Experience with version control and documentation is valued, along with the ability to

Formación

• Strong scientific or applied research background demonstrated through publications, thesis work, computational projects, or equivalent accomplishments.
• Practical Python experience for scientific computing, data analysis, modeling, or simulation.
• Familiarity with Python data-science libraries such as NumPy, pandas, SciPy, Matplotlib, scikit-learn, and Jupyter.

Responsabilidades

• Create and solve technically rigorous scientific problems within your area of expertise.
• Develop reproducible Python-based analyses, models, simulations, or data-processing workflows.
• Review scientific solutions and code for correctness, clarity, and completeness.

Conocimientos

Python programming

Data analysis

Scientific computing

Herramientas

NumPy

pandas

SciPy

Matplotlib

scikit-learn

Jupyter

Descripción del empleo

• Create and solve technically rigorous scientific problems within your area of expertise.
• Develop reproducible Python-based analyses, models, simulations, or data-processing workflows.
• Review scientific solutions and code for correctness, clarity, and completeness.

Requirements

• Strong scientific or applied research background demonstrated through publications, thesis work, computational projects, industry research, or equivalent accomplishments.
• Practical Python experience for scientific computing, data analysis, modeling, or simulation.
• Familiarity with tools such as NumPy, pandas, SciPy, Matplotlib, scikit-learn, Jupyter, or relevant domain-specific libraries.