Computational Protein Modeling Research Assistant

University of Texas

Utah

Presencial

USD 28.000 - 39.000

A tiempo parcial

Hace 6 días
Sé de los primeros/as/es en solicitar esta vacante
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Descripción de la vacante

The University of Texas at Austin's Oden Institute invites applications for a Research Engineering/ Scientist Assistant in Computational Biology and Protein Modeling. The role focuses on applying computational methods to protein structure, molecular interactions, and drug discovery.

Responsibilities include running large-scale studies, preparing structures, and collaborating with experimental teams and machine-learning researchers to validate predictions and share findings.

Formación

  • Bachelor's degree in biochemistry, molecular biology, or related field.
  • Understanding of protein structure, folding, and interactions.
  • Experience with visualization tools (PyMOL/ChimeraX) and databases.
  • Familiarity with Python and Linux for scripting and computation.

Responsabilidades

  • Apply protein docking and structure-prediction methods to systems.
  • Prepare protein, peptide, antibody, and small-molecule structures.
  • Run and analyze large-scale computational studies.
  • Compare predictions from different docking tools.
  • Collaborate with ML researchers to identify model limits.
  • Prepare figures, reports, and presentations.

Conocimientos

Biochemistry background
Molecular biology
Biophysics
Bioinformatics
Python scripting
Linux proficiency

Educación

Bachelor's Degree in a relevant field

Herramientas

PyMOL
ChimeraX
BLAST
Foldseek
Molecular modeling software

Descripción del empleo

The University of Texas at Austin's Oden Institute invites applications for a Research Engineering/ Scientist Assistant in Computational Biology and Protein Modeling. The role focuses on applying computational methods to protein structure, molecular interactions, and drug discovery.

Responsibilities include running large-scale studies, preparing structures, and collaborating with experimental teams and machine-learning researchers to validate predictions and share findings.

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