Thesis work: Characterization of pharmaceutical grade celluloses

Job description
Thesis work: Characterization of pharmaceutical grade celluloses by Imaging, 30-60 hp

Are you passionate about science and looking for a thesis work starting this autumn? Would you like to join a company that follows the science and turns ideas into life changing medicines? Then AstraZeneca might be the one for you!

At AstraZeneca we are committed to developing our people and sometimes this creates opporutinites for our people to work in different locations across the world. Are you ready to support our top talent by making this a great experience to help AstraZeneca continue to follow the science and turns ideas into life changing medicines?


With more than 2,400 employees from 50 countries, the vibrant Gothenburg site helps to support the entire life-cycle of AstraZeneca medicines, from drug discovery and clinical trials, to global commercialisation and product maintenance. Gothenburg is one of AstraZeneca’s three strategic, global R&D centres, alongside Cambridge and Gaithersburg, and plays a central role in our mission to deliver life-changing medicines to patients.

Responsibilities
This is an opportunity s to work in collaboration with Oral Product Development within AstraZeneca, Gothenburg. The team works in pre-clinical and all the way through to established commercial products. The focus of this project is to explore new ideas with characterization of chemically modified celluloses such as ethyl cellulose (EC), hydroxy-propyl cellulose (HPC), and hydroxy-propyl-methyl cellulose (HPMC) by photographic imaging of solid state spots obtained after separation by Size Exclusion Chromatography (SEC). We seek two master students for this project. There is one lab oriented track and one data science oriented track, detailed division of tasks will be distributed depending on the applicants preferences. The aim is to have separated primary focuses but a collaboration for the overall project. 

Modified celluloses are commonly used in pharmaceutical products and have various roles depending on their physical and chemical properties, both to speed up the release of drug from e.g. tablets by working as disintegrants and to reduce the speed of drug release through gel formation or film coating. As these celluloses are polymer molecules with biological origin, there are variations in the raw materials that influence the function during drug delivery even within a specific pharmaceutical grade.

The goal with this project is to evaluate a novel approach using imaging on molecular size fractions deposited as spots separated by SEC as a characterization tool. One subject is to correlate image information with results from reference measurements by IR, Raman, mass-spectrometry (MS), or time of flight secondary ion mass spectrometry (TOF-SIMS).  Another subject is to achieve automation for the analytical chemistry method from introduction of samples in the chromatographic system, deposition of spots, and development of the automation of the photographic imaging setup to accommodate the spots from the previous step. The aim is to submit findings as a scientific publication.

Exploratory and correlative deep learning and other machine leaning modelling for images will be employed to evaluate results and for correlation with the reference methods. 

These master thesis project will be a collaboration between a suitable department at your present university, and AstraZeneca Gothenburg. The location for the work will be in AstraZeneca in Mölndal with a start time not earlier than end of August 2021. The data science focussed part may involve working from home, depending on the Covid situation.

The project consists of:
Theoretical study and literature review
Decide appropriate chromatographic method, automation, machine learning pipeline
Build machine learning models that finds correlations between chromatographic/spectral variations and general molecular structure properties


Qualifications
For the lab oriented student: Successfully completed undergraduate studies in physics, chemistry, engineering, pharmacy or a related area.
For the data science oriented student: An interest in data science and machine learning, as well as successfully completed undergraduate studies in mathematics, technical / theoretical physics, chemistry, engineering, pharmacy or a related area. 


Application
Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.

Randstad Life Sciences is cooperating with AstraZeneca in this recruitment process. We only handle applications through Randstad’s website.

Deadline for application: 2021-04-25, selection and interviews will be ongoing. The position may be filled before the last day of application, therefore, apply as soon as possible.

For more information: Kerstin Karlsson Kerstin.karlsson@randstad.se

About the company
At AstraZeneca, all of our employees make a difference to patients’ lives every day. We operate in more than 100 countries around the world and are one of Sweden’s most important export companies.
 
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.