Logo of Huzzle

Predicting HCP-biologic interactions Internship

Applications are closed

  • Internship
    Full-time
    Placement Program
  • Data
    Research & Development
  • Cambridge

Requirements

  • Foundational knowledge in machine learning, computational biology, bioinformatics, or related fields.
  • Experience with machine learning frameworks and handling biological data.
  • Proficiency in with Python, especially for data manipulation and modelling tasks.
  • Experience working with large-scale datasets in bioinformatics.
  • Familiarity with protein-protein interaction prediction models and biological sequence analysis.
  • Experience with techniques related to mass spectrometry and proteomics.
  • Experience with protein language models.

Responsibilities

  • Collaborating with scientists to curate and prepare comprehensive training datasets for predicting HCP interactions with biologics.
  • Developing, training, and optimizing machine learning models to enhance prediction accuracy.
  • Validating model predictions using diverse data sources to ensure the robustness of AI tools.
  • Engaging with proteomics data to support the development of AI-driven analytical processes in drug manufacturing.

FAQs

What is the duration of the internship?

The internship is expected to last from June 2, 2025, to August 22, 2025, although this can be flexible.

What is the focus of the internship role?

The focus of the internship is on predicting host cell protein (HCP) interactions with biologic drugs, using advanced machine learning techniques and large proteomic datasets.

What kind of experience will I gain during this internship?

You will gain hands-on experience with machine learning applied to drug manufacturing challenges, collaborate with experts in proteomics and data analytics, and contribute to experimental data analysis.

What qualifications are required for this internship?

The ideal candidate should have foundational knowledge in machine learning, computational biology, or bioinformatics, experience with machine learning frameworks, proficiency in Python, and experience working with large-scale datasets in bioinformatics.

Are there any desirable skills for this position?

Yes, desirable skills include familiarity with protein-protein interaction prediction models, experience with mass spectrometry and proteomics, and knowledge of protein language models.

When will applications close for this internship?

Applications will be open until March 7, 2025.

How will I be supported during the internship?

You can expect mentorship from leading experts in the field and access to essential computational resources to help you succeed in your role.

How can I apply for the internship?

You can apply through the AstraZeneca careers website where the internship is listed.

Will there be opportunities to collaborate with others in this internship?

Yes, you will collaborate with scientists and experts within the team to prepare datasets and validate model predictions.

When can I expect to hear back regarding my application?

You can expect to hear from AstraZeneca by the end of March 2025 regarding your application status.

What science can do

Science & Healthcare
Industry
10,001+
Employees

Mission & Purpose

We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet. AstraZeneca is a global pharmaceutical company dedicated to improving the health and well-being of people worldwide. With a focus on innovative research and development, AstraZeneca develops and manufactures a wide range of prescription medicines, including treatments for cardiovascular, respiratory, oncology, and other therapeutic areas. Their aim is to transform the lives of patients by discovering, developing, and delivering innovative medicines that address unmet medical needs. AstraZeneca's purpose is to push the boundaries of science and collaborate with healthcare professionals, organisations, and communities to improve patient outcomes and contribute to the advancement of healthcare globally.