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AstraZeneca

Thesis Work, 30 Credits - Small Scale Recycling in API Production

AstraZeneca is hiring a Thesis Work, 30 Credits - Small Scale Recycling in API Production. Level rates it ; you can apply on AstraZeneca.

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  • On Level's scale, this role is Little AI ●○○○: aI is not part of the work.
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  • The role was posted in the last few days.

AI in this role

As we approach a more circular economy, an increased need for recycling has become all more apparent. But how can we implement more of it?

If you want to contribute to more sustainable pharmaceuticals through recycling, this project is for you!



About AstraZeneca:


AstraZeneca is a global, science-led, patient-centred biopharmaceutical company focusing on discovering, developing, and commercialising prescription medicines for some of the world’s most serious diseases. But we’re more than a global leading pharmaceutical company. At AstraZeneca, we're dedicated to being a Great Place to Work and empowering employees to push the boundaries of science and fuel their entrepreneurial spirit.


AstraZeneca and Site API:


At Site API in Snäckviken, Södertälje, we produce active pharmaceutical ingredients used in various therapeutic areas. When producing any kind of medicine, as in any chemical plant, waste is naturally generated. This usually takes the form of organic by-products, emissions or liquid waste, and needs to be taken care of in a safe manner.

The student will be included in the Technology team, consisting of process engineers and chemists, and will work cross-functionally with several functions.



About the Opportunity:


As a Thesis Worker at AstraZeneca, you’ll find an environment that’s full of unique opportunities and exciting challenges. Here, you’ll have the opportunity to pursue your areas of interest whilst equally developing a broad skillset and knowledge base to get the best out of your experience. You’ll be working on meaningful projects to make an impact and deliver real value for our patients and our business.



Thesis work description:


Solvent recycling is recognised as one of the most effective ways to reduce resource use in pharmaceutical manufacturing, with impacts across most sustainability metrics. The project aims to propose and delve deeper into processes that help us increase the circularity of solvents in one of AstraZeneca’s most emitting brands. This project is highly relevant to AstraZeneca’s current sustainability portfolio, with the goal of spreading to other sites in the network, including external collaboration partners.

The project should cover, but is not limited to, topics surrounding direct reuse of solvents in production, small-scale distillation for the purpose of reuse, and creating a strategy for the brand’s solvent recycling journey. The student should discuss how different chemical compositions in the streams of interest affect the ease of separation, and come up with a solution for the different scenarios.

Simulation software such as Aspen or MATLAB may be used to model a small system as a complement to the literature study. The study should also include an exploratory analysis of cross-contamination and its issues for recycling and reuse of solvents in a cGMP environment.

Some of the work will require the student to go and look in a factory environment, and laboratory studies may be relevant. The student is expected to be on site in Södertälje, with some flexibility.



Placement:  


This is an on-site position at AstraZeneca Södertälje.

AstraZeneca does not support with accommodations for this role



Structure:


  • Duration: Spring 2027
  • Credits: 30
  • One student


Essential Requirements:


  • Currently studying for a master’s degree in Chemical Engineering or a related discipline.
  • Has completed relevant coursework in chemical engineering, chemistry and/or sustainable production.
  • Understands separation technologies, particularly distillation, and is interested in solvent recovery and reuse.
  • Has experience with process-simulation tools, such as Aspen or MATLAB.
  • Is passionate about sustainability, recycling and contributing to a more circular economy.
  • Communicates clearly and confidently, both verbally and in writing, and has strong presentation skills.
  • Can work effectively with different teams and stakeholders, and demonstrates leadership potential and business acumen.

Desirable requirements


  • Knowledge of the pharmaceutical industry, API production or cGMP environments.
  • Familiarity with solvent recycling, cross-contamination considerations, or other relevant recovery technologies.


So, what’s next?


Apply today and take the chance to be part of making a difference, making connections, and gaining the tools and experience to open doors and fulfil your potential. We can´t wait to hear from you!

We welcome your application as soon as possible, but ahead of the scheduled closing date November 13th 2026. In the event that we identify suitable candidates ahead of the scheduled closing date, we reserve the right to withdraw the vacancy earlier than published.


Date Posted

30-okt.-2026

Closing Date

13-nov.-2026

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.

How we rate this

Thesis Work, 30 Credits - Small Scale Recycling in API Production at AstraZeneca rates 38 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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