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DMPK
Cambridge

Industrial Placement Student - DMPK

With over 35 nationalities and a range of backgrounds represented in our Benevolent team, we aim to build an inclusive environment where our people can bring their authentic selves to work, be respected for who they are and the exceptional work they do. We welcome and actively encourage applications from all sections of society and are committed to offering equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, marital, domestic or civil partnership status, sexual orientation, gender identity, parental status, disability, age, citizenship, or any other basis. We see our diversity as an asset as we tackle challenging problems that bridge the gap between drug discovery and technology.

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The Role

As a placement student in DMPK within BenevolentAI, you will be an integral part of the lab team contributing to one or more assays used to determine the “drug-like” properties of molecules synthesised at BenevolentAI. You will learn how we use data generated to prioritise the best compounds for further study and experiments and test these molecules in the lab using a range of physical chemistry, enzymology and cell-based biology & pharmacology techniques with the ultimate goal of identifying molecules suitable to test in clinical studies. We have active projects against several disease areas ranging from cancer to neurodegenerative diseases, autoimmune, rare diseases and others. Our projects are fast-moving and some may change by the time you start your placement so unfortunately, we can’t say which disease area you’d be working in but if you’d like to work in a specific disease area, let us know and we’ll see what might be possible.

You will have a dedicated supervisor to help you learn and get the best out of your placement. Good familiarity and experience with a range of lab-based experimental approaches are required, however, you’ll receive full training on the specific laboratory techniques you’ll be using along with associated health and safety considerations.

Primary Responsibilities

  • You will provide laboratory-based experimental input into one or more DMPK assays, helping to generate data in the lab to support our AI-generated therapeutic hypotheses..
  • You will receive training and learn to carry out experiments which could include: physical chemistry, enzymology and cellular assays of drug properties, molecular biology techniques and LC-MS end-points.
  • As a key part of the DMPK lab team, you’ll work closely with others from areas such as AI/Machine Learning, medicinal chemistry, in-vitro and in vivo pharmacology to coordinate experimental plans.
  • You’ll need to record all your experiments in our electronic lab notebook so we have an accurate record of all of your work.
  • You’ll have the chance to work with world-leading machine learning/AI scientists to learn how BenevolentAI is applying a range of AI approaches to drug discovery & development projects.
  • You’ll share your results with the rest of the team and beyond through written reports and oral presentations and you may also contribute to writing scientific publications or patent applications.
  • Your safety is very important to us so, following the safety guidance and training you’ll be given, you’ll be able to work in a way that keeps both you and all those around you safe in the lab at all times.

We are looking for someone with

  • An excellent understanding of your specialist area of study within the chemistry or biological sciences (eg biology, biochemistry, biomedical sciences, pharmacology), of human biology and how different medicines can be used to treat disease.
  • You’ll be on track to achieve a first-class or upper second class degree.
  • A desire to learn about how to discover new medicines to treat a range of diseases.
  • You’ll be a fast learner who thrives in a varied and fast-moving environment, learning, using new ideas and coming up with your own ideas every day.
  • We need someone who can demonstrate responsibility with the potential to work independently once trained, though someone who should also not be afraid to ask questions and seek clarification when needed.
  • You’ll enjoy working in a team and be able to easily communicate with others in your team and beyond to listen and understand the goals of the work, to share your results, to discuss scientific results & conclusions freely and to make new suggestions of future work.
  • You’ll be fun to work with and will play a full part in all aspects of your role and broader research at BenevolentAI!

The placement will be based at BenevolentAI’s Cambridge research centre based at the Babraham Research Campus, Cambridge. You’ll receive a competitive salary and benefits during your placement.

Closing date: February 11th 2022

We share a passion for being part of a mission that matters, and we are always looking for curious and collaborative people who share our values and want to be part of our journey.  If that sounds like a fit for you, hit the apply button and join us.

About us

BenevolentAI (AMS: BAI) is a leading, clinical-stage AI-enabled drug discovery and development company listed on the Euronext Amsterdam stock exchange. Through the combined capabilities of its AI platform, scientific expertise, and wet-lab facilities, BenevolentAI is well-positioned to deliver novel drug candidates with a higher probability of clinical success than those developed using traditional methods. The Benevolent Platform™ powers a growing in-house pipeline of 13 named drug programmes and over 10 exploratory programmes, and it maintains successful collaborations with AstraZeneca, as well as leading research and charitable institutions. BenevolentAI is headquartered in London, with a research facility in Cambridge (UK) and a further office in New York.

Want to do a little more research before you apply?

Head over to our Glassdoor page to learn about our benefits, culture and to find out what our team thinks about life at Benevolent. You can also find out more about us on LinkedIn and Twitter.

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Important Note

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