Aqemia is a next-gen pharmatech company generating one of the world's fastest-growing drug discovery pipeline. Our mission is to design fast innovative drug candidates for dozens of critical diseases. Our differentiation lies in our unique quantum and statistical mechanics algorithms fueling a generative artificial intelligence to design novel drug candidates.The disruptive speed and accuracy of our technological platform enables us to scale drug discovery projects just like tech projects.
We are now a team of 30 mission-driven, fast-paced people at the crossroads of Chemistry, Machine Learning, Physics and SoftwareEngineering, and have raised $12M with leading VCs.
Research Intern - Machine Learning and Deep Learning
Internship should last 4-6 months, and can start anytime before June 2022.
As a research intern, you will explore in depth a particular topic, all the way from literature review to testing your approach on proprietary data. We are willing to discuss the choice of the topic so that it best fits your expertise, but it could include :
- Generation of molecules in a constrained chemical space.
- Development of interpretable predictors of molecular properties.
- Multi Constraint Optimization strategies to generate molecules that satisfy several criteria.
- Representation learning on molecules : unsupervised or semi-supervised techniques to learn embeddings of molecules.
- Active learning strategies to decide when to acquire new data or to use model prediction.
- You are a Masters student in Computer Science or applied mathematics.
- Ideally with hands-on experience in representation learning, generative models, reinforcement learning.
- Interest for complex data such as graphs, text, 3D objects / point-clouds.
- Interest for unsupervised learning, low-data tasks and active learning.
- With experience in reading academic papers and exercising critical thinking on results.
Nice to have:
- Basic knowledge in biology and chemistry is a strong plus, but it is not required.
You should join us if…
- You are passionate about solving difficult problems on topics that really matter
- You are curious, willingful and dynamic
- You like working collaboratively in an interdisciplinary, fast-paced environment
- You want to join a small team to bring your own impact in drug discovery
To apply, send us your CV at firstname.lastname@example.org.