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Optimising biological activity and ADME properties, while minimising toxicity, are objectives when developing new compounds. Advanced machine learning methods are indispensable to this process. The project will develop and benchmark representation learning approaches, addressing their accuracy and explainability, using public and in-house data for endpoints ranging from chemical reactions to toxicity. The program will be done with the target users: large companies, regulatory agencies and SMEs.
What is offered
AiChemist is advertising 14 individual PhD projects: https://aichemist.eu/openpositions
AiChemist will provide structured training to its fellows through a combination of online courses and schools, strengthening European innovation capacity in the education of specialists in AI methods. In addition, the fellows will receive comprehensive training in transferable skills.
2 out of 14 of the advertised positions are based at Helmholtz Munich - the remaining 12 are based in various countries within Europe.
How is eligible
Candidates should have a:
- Have a Master's degree in computer science, physics, chemistry, or engineering with a sincere interest in biology and the life sciences.
- Have some prior expertise in one or more of the following fields: machine learning, modeling and simulation.
- Be excellent in oral and written English with good presentation skills.
- Possess strong interpersonal skills, excellent written and verbal communication, and the ability to work effectively both independently and in cross-functional teams.
- Be a highly creative person with outstanding problem-solving ability and the willingness to undertake challenging analysis tasks in a timely fashion.
Specific Requirements
- Excellent software engineering skills are essential. Programming skills in Python must be top-notch.
- Experience with relevant libraries (TensorFlow/PyTorch, the python scientific stack) is necessary.
- Good command of modern software development tools, from git to continuous integration pipelines, is an additional plus.
At the time of recruitment by the host organization, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organization for more than 12 months in the 3 years immediately prior to the reference date.
Descriptions of individual Positions
For each position, academic and industrial hosts are provided in the order of employment sequences. For example, DC1 will start in HMGU (Germany) and then continue his/her work in AstraZeneca (Sweden). Check this order with the mobility rule.
DC1: Improving accuracy and applicability domain of models using representation learning
Academic Host: HMGU (first 18 months), Industrial Host: AstraZeneca (second 18 months), Planned Secondment: Bayer
DC2: Using XAI to Develop Hybrid Chemotypes
Academic Host: HMGU (first 12 months), Industrial Hosts: MolNet (12 months) and Pfizer (final 12 months), Planned Secondment: Bayer
DC3: Predicting chemical stability and degradation rates of the compounds in acidic, basic, oxidative and reductive media using combined metadynamics-MD and ML approach
Academic Host: UCPH (first 18 months), Industrial Host: AstraZeneca (second 18 months). Planned secondment: UNISTRA
DC4: Prediction of optimal reaction conditions using Artificial Intelligence tools
Industrial Host: AstraZeneca (first 18 months), Academic Host: UNISTRA (second 18 months), Planned secondment: UCPH.
DC5: Multi-task Neural Network reactivity prediction using in-silico simulations and synthesis experimental data
Academic Host: ULEI (first 18 months), Industrial Host: AstraZeneca (second 18 months). Planned secondment: UCPH
DC6: Advanced ML methods to predict and understand toxicity of drugs
Academic Host: IRFMN (first 18 months), Industrial Host: Bayer (second 18 months). Planned secondment: HMGU
DC7: Generative language models for the design of tailored chemical transformations
Academic Host: CSIC (first 18 months), Industrial Host: Bayer (second 18 months). Planned secondment: EPFL
DC8: Modeling drug response in image-based screens as function of chemical space
Academic Host: TUM (first 18 months), Industrial Host: Bayer (second 18 months). Planned secondment: Pfizer
DC9: Explainable active learning for multi-objective de novo design
Academic Host: TU/e (first 18 months), Industrial Host: Sanofi (second 18 months). Planned secondment: Bayer
DC10: Simple quantum descriptors for actionable insights on ADMET-related properties
Academic Host: ENS-PSL (first 18 months), Industrial Host: Sanofi (second 18 months). Planned secondment: IRFMN
DC11: Multi-instance explainable learning for decoding stereo-dependent biological effects
Academic Host: UNISTRA (first 18 months), Industrial Host: Sanofi (second 18 months). Planned secondment: ENS-PSL
DC12: Learning chemically explainable multi-task molecular representations
Academic Host (employer): USI (36 months) Industrial Secondments: Pfizer and Bayer. The funding for this DC will come from a Swiss funding body rather than the EU.
DC13: Explainable chemical representations and models for reaction outcome predictions
Academic Host: EPFL (36 months), Industrial secondment: Pfizer. The funding for this DC will come from a Swiss funding body rather than the EU.
DC14: Development of XAI models for beyond rule of 5 chemical space molecules
Academic Host: KIT (36 months). The funding for this DC will come from a Korean funding body rather than the EU.
How to apply
- Make sure that you satisfy the eligibility and mobility rules!
- Prepare your profile and provide sufficient details about your educational and work background, proof of your education (or expected award date of your MSc/Diploma), your CV, and motivation letter.
- Submit your application to apply@aichemist.eu before the deadline of 1st September 2023. The screening will begin immediately, so please do not wait until the deadline to submit your application.
Note: A candidate may apply for up to three of the listed positions, as long as they have the relevant background and expertise. If you wish to apply for more than one position, please rank the positions according to your preference i.e. indicate your first, second and third choices.
This project is funded by the European Union’s Horizon research and innovation programme unde grant agreement No 101120466 (under negotiations, expected to start September 1st, 2023), and it is Horizon Europe (HORIZON) Marie Skłodowska-Curie Actions Doctoral Networks (MSCA-DN).