Roche
Internship – Machine Learning for Antibody Discovery from Sequencing Data(m/w/x)
Full-timeInternshipOn-site
Basel
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RORoche
Internship – Machine Learning and Molecular Simulation for Functional Antibody Characterisation(m/w/x)
Basel
Full-timeInternshipOn-site
AI/ML
Nejo AI Summary
Developing novel data-driven sampling methods for antibody structural ensembles. Master's or PhD in a technical field with simulation software and neural network experience required. HPC environments and Python proficiency needed.
Requirements
- Recent Master's graduate (within 12 months) or enrolled PhD student in Bioinformatics, Computational Biology, Computational Physics, Computer Science, Statistics, Applied Mathematics, or related technical field
- Theoretical knowledge and practical experience with enhanced sampling methods or other simulation techniques and their application to protein systems
- Extensive practical experience with at least one simulation software suite (e.g. Amber, OpenMM, Gromacs, Plumed)
- Some experience with building and training neural networks within at least one DL framework (preferably pytorch), keen to build models from scratch or adapt architectures from literature
- Fluent in high performance computing (HPC) environments and comfortable with at least one programming language (ideally Python) for building complex orchestrating pipelines
- Excellent communication and interpersonal skills
- Maintained enrollment at a university for the full duration of the internship
- Non-EU/EFTA citizens must provide a certificate from the university stating that an internship is mandatory and must be continuously enrolled for the whole duration of the internship
Tasks
- Develop novel data-driven sampling methods
- Evaluate machine learning-based sampling techniques
- Characterize antibody structural ensembles accurately
- Generate large-scale synthetic datasets
- Orchestrate efficient use of compute resources
- Contribute to real drug-discovery projects
- Collaborate with teams in Basel, New York, and San Francisco
- Drive or contribute to publications
- Present results at internal and external venues
Education
- Currently in higher education
Languages
- English – Native
Tools & Technologies
- Amber
- OpenMM
- Gromacs
- Plumed
- pytorch
- Python
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Roche and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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RORoche
Internship – Machine Learning and Molecular Simulation for Functional Antibody Characterisation(m/w/x)
Basel
Full-timeInternshipOn-site
AI/ML
Nejo AI Summary
Developing novel data-driven sampling methods for antibody structural ensembles. Master's or PhD in a technical field with simulation software and neural network experience required. HPC environments and Python proficiency needed.
Requirements
- Recent Master's graduate (within 12 months) or enrolled PhD student in Bioinformatics, Computational Biology, Computational Physics, Computer Science, Statistics, Applied Mathematics, or related technical field
- Theoretical knowledge and practical experience with enhanced sampling methods or other simulation techniques and their application to protein systems
- Extensive practical experience with at least one simulation software suite (e.g. Amber, OpenMM, Gromacs, Plumed)
- Some experience with building and training neural networks within at least one DL framework (preferably pytorch), keen to build models from scratch or adapt architectures from literature
- Fluent in high performance computing (HPC) environments and comfortable with at least one programming language (ideally Python) for building complex orchestrating pipelines
- Excellent communication and interpersonal skills
- Maintained enrollment at a university for the full duration of the internship
- Non-EU/EFTA citizens must provide a certificate from the university stating that an internship is mandatory and must be continuously enrolled for the whole duration of the internship
Tasks
- Develop novel data-driven sampling methods
- Evaluate machine learning-based sampling techniques
- Characterize antibody structural ensembles accurately
- Generate large-scale synthetic datasets
- Orchestrate efficient use of compute resources
- Contribute to real drug-discovery projects
- Collaborate with teams in Basel, New York, and San Francisco
- Drive or contribute to publications
- Present results at internal and external venues
Education
- Currently in higher education
Languages
- English – Native
Tools & Technologies
- Amber
- OpenMM
- Gromacs
- Plumed
- pytorch
- Python
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Roche and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Roche
Industry
Pharmaceuticals
Description
The company is dedicated to advancing science and ensuring access to healthcare for everyone.
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