Postdoctoral Fellow: Molecular Modelling & Machine Learning
Location: Gothenburg, Sweden
Competitive Salary, Bonus & Benefits Package

Do you have expertise in, and passion for Machine Learning, Computational Chemistry, Chemo-informatics or Immuno-informatics? Would you like to apply your expertise to impact computational immunology assessment in a company that follows the science and turns ideas into life-changing medicines? Then Astra Zeneca might be the one for you!

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

About our Postdoc Program
Bring your expertise, apply your knowledge, follow the science, and make a difference. Astra Zeneca’s Postdoc Program is for self-motivated individuals looking to deliver exciting projects in an encouraging, engaging, and innovative environment. You will collaborate with multidisciplinary scientific teams from a diverse set of backgrounds and a world-class academic mentor specifically aligned to your project. Our postdocs are respected as specialists, encouraged to speak up, and supported to share their research at conferences, publish papers, achieve their goals.
Make a more meaningful impact in your career, with greater ownership and accountability to contribute whilst making a difference to the lives of our patients!
This is a 3-year program.

About the Opportunity
In this exciting Postdoctoral Fellow position, you will research and develop computational tools for the prediction of immunogenicity risk for peptides and proteins incorporating non-natural amino acids.
This project is supported by the Computational Chemistry team within the Early Respiratory & Immunology function, which is centered on delivering life-changing products that improve health and fight and cure diseases. This team sits within the Medicinal Chemistry Department and supports all the pipeline development projects within the therapeutic area for small molecules and new modalities (peptides and oligonucleotides). The team also collocates in the Gothenburg in-silico Center of Excellence with dozens of other experts in the use of computational methods for drug-development.
This project is also supported by the Clinical Pharmacology & Safety Sciences (CPSS) group that works across all of Astra Zeneca’s therapy areas from early-stage drug discovery to late-stage clinical development.
The project will be in collaboration with an academic institution specializing in computational immunogenicity tools.

What you will do
Our Postdoctoral Fellow will conduct ground-breaking research using structure-based modelling and machine learning methods for prediction of immunogenicity. You will develop computational tools for immunogenicity assessment suitable for peptides and proteins incorporating non-natural amino acids. This will involve structural modelling of peptide-protein interactions, advanced computational methods to calculate binding affinities (e.g. Free Energy Perturbation) and utilizing state-of-the art machine learning techniques (e.g. transfer learning neutral networks).

Key Duties & Responsibilities:

• Research, design and implement innovative computational tools for immunogenicity assessment of peptides and proteins incorporating non-natural amino acids.
  
• Lead and drive the project under the supervision of cross-functional Astra Zeneca team and in collaboration with the academic expert.
  
• Plan, write, publish, and present high-quality scientific papers.
  
• Participate in function and department activities in Astra Zeneca, learn about drug discovery and development processes and get exposure to new scientific advances.
  

Essential Requirements

• Ph D degree, or equivalent in computational biology / chemistry, or cheminformatics (with a significant data science component), or mathematics / machine learning.
  
• Experience with structure-based molecular modelling.
  
• Good knowledge of machine learning methods and data science concepts.
  
• Proficiency in programming (preferably Python).
  

Our Postdoc programme is aimed at individuals with a strong publication record who are either:

• Within 5-years of obtaining their doctoral degree (Ph D, DVM, or MD) - Ph D already awarded, or
  
• Students, soon to obtain doctoral degrees (the Ph D must be awarded within 6 months of joining Astra Zeneca).
  

Desirable Requirements

• Proven knowledge of one or more of the following areas: Immuno-informatics, Computational biology, Cheminformatics or Computational chemistry
  
• Excellent written and oral communication skills
  
• Strong planning, organizational and time management skills
  
• Ability to work effectively in a multidisciplinary research environment.
  

Reasons to Apply
You will work with the innovative technology in a highly collaborative environment to apply novel ideas to answer important questions addressing the safety of biological medicines. Immunogenicity represents a potential undesired safety risk for new biological therapeutic modalities. New modalities such as peptide-based therapeutics and antibody-drug conjugates frequently include non-natural amino acids. It is currently not possible to assess the immunogenicity risks resulting from such modifications computationally. By developing in silico tools to assess this risk you will help to deliver efficacious and safe medicines for our patients.
You will have several opportunities to work in collaboration with supportive cross-functional internal and external academic partners and engage with the external community, through publications in high-quality journals and presentations at conferences.
As a respected specialist, we will empower you to take appropriate risks, lead the project and “run with it”, along with providing you will all the support you need.

Why Astra Zeneca?
The unique positioning of this postdoc within a pharmaceutical company such as AZ, means that you will be able to access large datasets which are not available in the public domain. Moreover, you’ll have the satisfaction of knowing that methods and techniques you develop will not only be published but will also be used within AZ after the postdoc position concludes, in real-life projects - and that ultimately the work you do will directly impact patients’ lives!

On-Site Working
The position is located at our research site in Gothenburg, Sweden.
As a key member of a research group and taking a leading role on a Postdoctoral project, we expect regular attendance - a minimum of 3 days per week on the Astra Zeneca campus. For laboratory-based scientists, this may be higher.
Given the unique positioning of our Postdoctoral researchers, you may have an additional opportunity to spend some time with an external academic mentor. In this instance, you may be required to spend some of your time working alongside them at their facility as driven by the research project.
Your flexibility to meet the needs of the research and the scope of the project is required, and we also look to support you with flexibility in accordance with our standard work policies.

Next Steps
If your passion is science and you want to be part of a team that makes a bigger impact on patients’ lives, then there’s no better place to be.
This advert will be running from August 26, 2024 and we welcome your application as soon as possible, but ahead of the scheduled closing date of September 15, 2024. 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.

Find Out More
Astra Zeneca: https://www.astrazeneca.com/
AZ’s Postdoc Program: https://careers.astrazeneca.com/postdocs-astrazeneca
Life at Astra Zeneca: https://careers.astrazeneca.com/life-at-astrazeneca
This is what we’re made of: https://www.youtube.com/watch?v=pm Fx_jr2e Fo&t=2s